From f87d6278efb239bae66f58c08927967effd9b81d Mon Sep 17 00:00:00 2001
From: leoniewgnr <42536262+leoniewgnr@users.noreply.github.com>
Date: Mon, 15 May 2023 07:51:07 -0700
Subject: [PATCH 1/2] [docs] prettified application examples (#1328)
---
.../energy_hospital_load.ipynb | 5504 +---------
.../energy_solar_pv.ipynb | 9203 ++---------------
poetry.lock | 458 +-
3 files changed, 1400 insertions(+), 13765 deletions(-)
diff --git a/docs/source/how-to-guides/application-examples/energy_hospital_load.ipynb b/docs/source/how-to-guides/application-examples/energy_hospital_load.ipynb
index 7b1ef090f..56f5ae200 100644
--- a/docs/source/how-to-guides/application-examples/energy_hospital_load.ipynb
+++ b/docs/source/how-to-guides/application-examples/energy_hospital_load.ipynb
@@ -1,6 +1,7 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"collapsed": false
@@ -10,6 +11,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -40,32 +42,13 @@
"import pandas as pd\n",
"from neuralprophet import NeuralProphet, set_log_level\n",
"\n",
- "# set_log_level(\"ERROR\")"
+ "set_log_level(\"ERROR\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [],
- "source": [
- "data_location = \"https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/\"\n",
- "\n",
- "\n",
- "sf_load_df = pd.read_csv(data_location + \"energy/SF_hospital_load.csv\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 140
- },
- "id": "TvrgKVWIuxFJ",
- "outputId": "99908203-2022-456a-9d05-73c3d0e6731e"
- },
"outputs": [
{
"data": {
@@ -119,16 +102,21 @@
"2 2015-01-01 03:00:00 779.357338"
]
},
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
+ "data_location = \"https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/\"\n",
+ "\n",
+ "\n",
+ "sf_load_df = pd.read_csv(data_location + \"energy/SF_hospital_load.csv\")\n",
"sf_load_df.head(3)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -138,7 +126,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -160,5039 +148,155 @@
"id": "s7faUgnrvGFN",
"outputId": "50da2450-767f-4e3b-f03d-d03226d24ff8"
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.config.__post_init__) - Note: Trend changepoint regularization is experimental.\n",
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.989% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
- "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
- "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n",
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.988% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
- "INFO - (NP.utils.set_auto_seasonalities) - Disabling yearly seasonality. Run NeuralProphet with yearly_seasonality=True to override this.\n",
- "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 32\n",
- "INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 109\n"
- ]
- },
- {
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- "Training: 0it [00:00, ?it/s]"
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- "metadata": {},
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+ "outputs": [],
+ "source": [
+ "m = NeuralProphet(\n",
+ " weekly_seasonality=6,\n",
+ " daily_seasonality=10,\n",
+ " trend_reg=1,\n",
+ " learning_rate=0.01,\n",
+ ")\n",
+ "df_train, df_test = m.split_df(sf_load_df, freq=\"H\", valid_p=1.0 / 12)\n",
+ "\n",
+ "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"bar\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 111
},
+ "id": "OnEPYrkscVtf",
+ "outputId": "fe0218f4-0fbe-4d74-86f3-0e74195681a7"
+ },
+ "outputs": [
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+ "text/html": [
+ "
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+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " MAE_val | \n",
+ " RMSE_val | \n",
+ " Loss_val | \n",
+ " RegLoss_val | \n",
+ " epoch | \n",
+ " MAE | \n",
+ " RMSE | \n",
+ " Loss | \n",
+ " RegLoss | \n",
+ "
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+ " \n",
+ " \n",
+ " 108 | \n",
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+ " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
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+ "\n",
+ " Loss RegLoss \n",
+ "108 0.004436 0.0 "
]
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+ }
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+ "source": [
+ "metrics.tail(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "m = NeuralProphet(\n",
- " weekly_seasonality=6,\n",
- " daily_seasonality=10,\n",
- " trend_reg=1,\n",
- " learning_rate=0.01,\n",
- ")\n",
- "df_train, df_test = m.split_df(sf_load_df, freq=\"H\", valid_p=1.0 / 12)\n",
- "\n",
- "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"plot\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 111
- },
- "id": "OnEPYrkscVtf",
- "outputId": "fe0218f4-0fbe-4d74-86f3-0e74195681a7"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " MAE_val | \n",
- " RMSE_val | \n",
- " Loss_val | \n",
- " RegLoss_val | \n",
- " epoch | \n",
- " MAE | \n",
- " RMSE | \n",
- " Loss | \n",
- " RegLoss | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 108 | \n",
- " 60.81974 | \n",
- " 81.957932 | \n",
- " 0.009394 | \n",
- " 0.0 | \n",
- " 108 | \n",
- " 46.276749 | \n",
- " 63.550903 | \n",
- " 0.004476 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- "
\n",
- "
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- " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
- "108 60.81974 81.957932 0.009394 0.0 108 46.276749 63.550903 \n",
- "\n",
- " Loss RegLoss \n",
- "108 0.004476 0.0 "
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- },
- "execution_count": 5,
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- "metrics.tail(1)"
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- "execution_count": 6,
- "metadata": {
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- "text": [
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.988% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.988% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
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- "text/plain": [
- "Predicting: 251it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
- ]
- }
- ],
- "source": [
- "forecast = m.predict(df_train)\n",
- "fig = m.plot(forecast)"
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- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "colab": {
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- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.863% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.863% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
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- ]
- }
- ],
- "source": [
- "forecast = m.predict(df_test)\n",
- "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
- "fig = m.plot(forecast[-7 * 24 :])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
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- "id": "G0E4dLGxcbsO",
- "outputId": "9590ed3b-6e76-4fdd-d4bf-c310c04ac7ec"
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- {
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- "WARNING - (NP.forecaster.plot_parameters) - highlight_forecast_step_n is ignored since autoregression not enabled.\n"
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- }
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- "source": [
- "fig_param = m.plot_parameters()"
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- },
- {
- "cell_type": "markdown",
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- "source": [
- "### 1-step ahead forecast with Auto-Regression"
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- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
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- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
- "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
- "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n",
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.988% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
- "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 32\n",
- "INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 109\n"
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "m = NeuralProphet(\n",
- " growth=\"off\",\n",
- " yearly_seasonality=False,\n",
- " weekly_seasonality=False,\n",
- " daily_seasonality=False,\n",
- " n_lags=3 * 24,\n",
- " ar_reg=1,\n",
- " learning_rate=0.01,\n",
- ")\n",
- "df_train, df_test = m.split_df(sf_load_df, freq=\"H\", valid_p=1.0 / 12)\n",
- "\n",
- "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"plot\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 111
- },
- "id": "OnEPYrkscVtf",
- "outputId": "fe0218f4-0fbe-4d74-86f3-0e74195681a7"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " MAE_val | \n",
- " RMSE_val | \n",
- " Loss_val | \n",
- " RegLoss_val | \n",
- " epoch | \n",
- " MAE | \n",
- " RMSE | \n",
- " Loss | \n",
- " RegLoss | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 108 | \n",
- " 24.245213 | \n",
- " 37.304489 | \n",
- " 0.002818 | \n",
- " 0.000873 | \n",
- " 108 | \n",
- " 23.824387 | \n",
- " 35.93779 | \n",
- " 0.002335 | \n",
- " 0.000872 | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
- "108 24.245213 37.304489 0.002818 0.000873 108 23.824387 35.93779 \n",
- "\n",
- " Loss RegLoss \n",
- "108 0.002335 0.000872 "
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metrics.tail(1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.988% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.988% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "59cb802e86a846fa94bea032e067159c",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Predicting: 249it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
- ]
- }
- ],
- "source": [
- "forecast = m.predict(df_train)\n",
- "fig = m.plot(forecast)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 440
- },
- "id": "5v-4bpNUvELW",
- "outputId": "089e3649-a238-4ec1-9593-32c2844d0ee4"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.874% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.875% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "03cb25ddf02045298969035ee76e7ccd",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Predicting: 249it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
- ]
- }
- ],
- "source": [
- "forecast = m.predict(df_test)\n",
- "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
- "fig = m.plot(forecast[-7 * 24 :])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig_param = m.plot_parameters()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1 step ahead forecast with AR-Net: Using a Neural Network\n",
- "Here, we will use the power of a neural Network to fit non-linear patterns."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
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- "Validation: 0it [00:00, ?it/s]"
+ },
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "forecast = m.predict(df_train)\n",
+ "m.set_plotting_backend(\"plotly-static\")\n",
+ "m.plot(forecast)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 440
},
+ "id": "5v-4bpNUvELW",
+ "outputId": "089e3649-a238-4ec1-9593-32c2844d0ee4"
+ },
+ "outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "06fac8b961bc4da1a355768aefe7a1f4",
+ "model_id": "6583954689264d95a16ce1914af1b322",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "Predicting: 251it [00:00, ?it/s]"
]
},
"metadata": {},
@@ -5200,55 +304,176 @@
},
{
"data": {
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- "model_id": "b31e1cf0d1124b12bce559e0d29cf2fd",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "forecast = m.predict(df_test)\n",
+ "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
+ "m.plot(forecast[-7 * 24 :])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
},
+ "id": "G0E4dLGxcbsO",
+ "outputId": "9590ed3b-6e76-4fdd-d4bf-c310c04ac7ec"
+ },
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "3e7f56c8f3f94a2bac7749c883e74110",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "m.plot_parameters()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eG80MrTSeM0r"
+ },
+ "source": [
+ "### 1-step ahead forecast with Auto-Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5KIVKB_teK-n",
+ "outputId": "c8dc6c51-2257-4f1f-f394-b5b941fb34c0"
+ },
+ "outputs": [],
+ "source": [
+ "m = NeuralProphet(\n",
+ " growth=\"off\",\n",
+ " yearly_seasonality=False,\n",
+ " weekly_seasonality=False,\n",
+ " daily_seasonality=False,\n",
+ " n_lags=3 * 24,\n",
+ " ar_reg=1,\n",
+ " learning_rate=0.01,\n",
+ ")\n",
+ "df_train, df_test = m.split_df(sf_load_df, freq=\"H\", valid_p=1.0 / 12)\n",
+ "\n",
+ "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"bar\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 111
},
+ "id": "OnEPYrkscVtf",
+ "outputId": "fe0218f4-0fbe-4d74-86f3-0e74195681a7"
+ },
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1d24e446aa8548aaba117d9e5e66b771",
- "version_major": 2,
- "version_minor": 0
- },
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " MAE_val | \n",
+ " RMSE_val | \n",
+ " Loss_val | \n",
+ " RegLoss_val | \n",
+ " epoch | \n",
+ " MAE | \n",
+ " RMSE | \n",
+ " Loss | \n",
+ " RegLoss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 108 | \n",
+ " 24.246571 | \n",
+ " 37.293633 | \n",
+ " 0.002822 | \n",
+ " 0.000877 | \n",
+ " 108 | \n",
+ " 23.822706 | \n",
+ " 35.911766 | \n",
+ " 0.002339 | \n",
+ " 0.000877 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
+ "108 24.246571 37.293633 0.002822 0.000877 108 23.822706 35.911766 \n",
+ "\n",
+ " Loss RegLoss \n",
+ "108 0.002339 0.000877 "
]
},
+ "execution_count": 9,
"metadata": {},
- "output_type": "display_data"
- },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "metrics.tail(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "5c2b5e79a8a648ddba61010237e8dfa0",
+ "model_id": "118a56e442da432fa63256efb2b73b04",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "Predicting: 249it [00:00, ?it/s]"
]
},
"metadata": {},
@@ -5256,27 +481,41 @@
},
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "c18280ea1f11427ba200b47b25df8836",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "forecast = m.predict(df_train)\n",
+ "m.set_plotting_backend(\"plotly-static\")\n",
+ "m.plot(forecast)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 440
},
+ "id": "5v-4bpNUvELW",
+ "outputId": "089e3649-a238-4ec1-9593-32c2844d0ee4"
+ },
+ "outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f9f79abdde414c34890e530015654cef",
+ "model_id": "2355ea9b6af34a559b2b0cbde592751b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "Predicting: 249it [00:00, ?it/s]"
]
},
"metadata": {},
@@ -5284,29 +523,59 @@
},
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "5ed660007959471e8850f57161a2a62f",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
- },
+ }
+ ],
+ "source": [
+ "forecast = m.predict(df_test)\n",
+ "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
+ "m.plot(forecast[-7 * 24 :])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
{
"data": {
- "image/png": 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",
- "text/plain": [
- ""
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
}
],
+ "source": [
+ "m.plot_parameters()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1 step ahead forecast with AR-Net: Using a Neural Network\n",
+ "Here, we will use the power of a neural Network to fit non-linear patterns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-aqM32pCfjEG",
+ "outputId": "86bc014e-1e90-48e0-ac25-c9597a0fbcfe"
+ },
+ "outputs": [],
"source": [
"m = NeuralProphet(\n",
" growth=\"off\",\n",
@@ -5319,12 +588,12 @@
")\n",
"df_train, df_test = m.split_df(sf_load_df, freq=\"H\", valid_p=1.0 / 12)\n",
"\n",
- "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"plot\")"
+ "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"bar\")"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -5369,14 +638,14 @@
" \n",
" \n",
" 108 | \n",
- " 7.110816 | \n",
- " 11.072184 | \n",
- " 0.000171 | \n",
+ " 7.14312 | \n",
+ " 10.696775 | \n",
+ " 0.00016 | \n",
" 0.0 | \n",
" 108 | \n",
- " 6.158021 | \n",
- " 8.984686 | \n",
- " 0.000092 | \n",
+ " 6.197587 | \n",
+ " 9.060362 | \n",
+ " 0.000093 | \n",
" 0.0 | \n",
"
\n",
" \n",
@@ -5384,14 +653,14 @@
""
],
"text/plain": [
- " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
- "108 7.110816 11.072184 0.000171 0.0 108 6.158021 8.984686 \n",
+ " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
+ "108 7.14312 10.696775 0.00016 0.0 108 6.197587 9.060362 \n",
"\n",
" Loss RegLoss \n",
- "108 0.000092 0.0 "
+ "108 0.000093 0.0 "
]
},
- "execution_count": 16,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -5402,25 +671,15 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 15,
"metadata": {
"tags": []
},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.988% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.988% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
- ]
- },
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "0cf8c8fbe73442c09d293ef28e0b3d8c",
+ "model_id": "a38aecde147c47aa872147fab55d30c9",
"version_major": 2,
"version_minor": 0
},
@@ -5432,21 +691,24 @@
"output_type": "display_data"
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
- ]
+ "data": {
+ "image/svg+xml": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
"forecast = m.predict(df_train)\n",
- "fig = m.plot(forecast)"
+ "m.set_plotting_backend(\"plotly-static\")\n",
+ "m.plot(forecast)"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -5456,20 +718,10 @@
"outputId": "089e3649-a238-4ec1-9593-32c2844d0ee4"
},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.874% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
- "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.875% of the data.\n",
- "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
- ]
- },
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "83196bf1bd69455ca9044f8fbce1241e",
+ "model_id": "f79f39c274b44a41b347fcdbac0f3391",
"version_major": 2,
"version_minor": 0
},
@@ -5481,26 +733,38 @@
"output_type": "display_data"
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
- ]
+ "data": {
+ "image/svg+xml": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
"forecast = m.predict(df_test)\n",
"m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
- "fig = m.plot(forecast[-7 * 24 :])"
+ "m.plot(forecast[-7 * 24 :])"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 17,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "fig_comp = m.plot_components(forecast[-7 * 24 :])"
+ "m.plot_components(forecast[-7 * 24 :])"
]
}
],
diff --git a/docs/source/how-to-guides/application-examples/energy_solar_pv.ipynb b/docs/source/how-to-guides/application-examples/energy_solar_pv.ipynb
index b19aa0205..886f3e840 100644
--- a/docs/source/how-to-guides/application-examples/energy_solar_pv.ipynb
+++ b/docs/source/how-to-guides/application-examples/energy_solar_pv.ipynb
@@ -1,6 +1,7 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"collapsed": false
@@ -10,6 +11,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -19,7 +21,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -42,7 +44,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -52,6 +54,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "0Krto6fIvHit"
@@ -66,7 +69,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -88,44 +91,112 @@
"id": "s7faUgnrvGFN",
"outputId": "50da2450-767f-4e3b-f03d-d03226d24ff8"
},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "d8110a189efa499a98d6b7b1b7543ba2",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Training: 0it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "outputs": [],
+ "source": [
+ "m = NeuralProphet(\n",
+ " yearly_seasonality=3,\n",
+ " weekly_seasonality=False,\n",
+ " daily_seasonality=8,\n",
+ " growth=\"off\",\n",
+ " learning_rate=0.1,\n",
+ ")\n",
+ "df_train, df_test = m.split_df(sf_pv_df, freq=\"H\", valid_p=0.10)\n",
+ "\n",
+ "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"bar\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 111
},
+ "id": "OnEPYrkscVtf",
+ "outputId": "fe0218f4-0fbe-4d74-86f3-0e74195681a7"
+ },
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "5aaf3c9c83694078b8adc6de6fdc441d",
- "version_major": 2,
- "version_minor": 0
- },
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " MAE_val | \n",
+ " RMSE_val | \n",
+ " Loss_val | \n",
+ " RegLoss_val | \n",
+ " epoch | \n",
+ " MAE | \n",
+ " RMSE | \n",
+ " Loss | \n",
+ " RegLoss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 108 | \n",
+ " 131.362961 | \n",
+ " 146.889999 | \n",
+ " 0.013868 | \n",
+ " 0.0 | \n",
+ " 108 | \n",
+ " 92.142319 | \n",
+ " 118.028023 | \n",
+ " 0.006694 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE \\\n",
+ "108 131.362961 146.889999 0.013868 0.0 108 92.142319 \n",
+ "\n",
+ " RMSE Loss RegLoss \n",
+ "108 118.028023 0.006694 0.0 "
]
},
+ "execution_count": 5,
"metadata": {},
- "output_type": "display_data"
- },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "metrics.tail(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "0e1ed9b170bc4f3994480d3490a6bc71",
+ "model_id": "73419cb1e8b046cdb45e750bb7e0a7fa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "Predicting: 247it [00:00, ?it/s]"
]
},
"metadata": {},
@@ -133,41 +204,68 @@
},
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9753b7648b44439bbb08954d7182fe10",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "forecast = m.predict(sf_pv_df)\n",
+ "m.set_plotting_backend(\"plotly-static\")\n",
+ "m.plot(forecast)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
},
+ "id": "G0E4dLGxcbsO",
+ "outputId": "9590ed3b-6e76-4fdd-d4bf-c310c04ac7ec",
+ "tags": []
+ },
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "36e17a094ce74457aa818179f01c87f0",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "m.plot_parameters()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 440
},
+ "id": "5v-4bpNUvELW",
+ "outputId": "089e3649-a238-4ec1-9593-32c2844d0ee4"
+ },
+ "outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7e495662b6034523b09e043dec478932",
+ "model_id": "b770561e27fd4677bad06f1237b0642e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "Predicting: 247it [00:00, ?it/s]"
]
},
"metadata": {},
@@ -175,8298 +273,189 @@
},
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "e0559e4dfda44ec6982fde660e861cb6",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "forecast = m.predict(df_test)\n",
+ "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
+ "m.plot(forecast)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 440
},
+ "id": "pmx8KTT0cgyR",
+ "outputId": "16062765-4e93-43c6-c434-857ad4b19008"
+ },
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "5db1c381aee74178b27fc9d7c21ebbd3",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "m.plot(forecast[-48:])"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1-step ahead forecast with Auto-Regresseion\n",
+ "In this second section, we will train a 1-step ahead forecaster on solar irradiance data (that can be a proxy for solar PV production). We can train this forecaster considering the privious 24 steps and disabling trend and seasonality.\n",
+ "\n",
+ "The paramters that we can modify are the number of lags and the AR sparsity."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 148,
+ "referenced_widgets": [
+ "4ac0917121f8498698e087259b787dcf",
+ "94108fe9090f47c7ba2216479e0d3fac",
+ "2d8235496ec642af8192f52d9f2692b1",
+ "c94a8ae41b994c55a96ad44806b0f1c7",
+ "24bf564f55644476911a6cf004a395e7",
+ "87c170d1e00742a29e7f797e98c49cc2",
+ "8a192ccc35e94e9f8be85898ed583e2c",
+ "9467345334da47a8beadc770feef952a",
+ "dc468cd35d2b4f0e8eb287689ac15412",
+ "f35fc9cbd82c4187a4cdc08c3ac26998",
+ "aab682cd3df24821a80331720f7c24e5"
+ ]
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9930d974c1da432b8d980c4592acd3c1",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "id": "s7faUgnrvGFN",
+ "outputId": "50da2450-767f-4e3b-f03d-d03226d24ff8"
+ },
+ "outputs": [],
+ "source": [
+ "m = NeuralProphet(\n",
+ " growth=\"off\",\n",
+ " yearly_seasonality=False,\n",
+ " weekly_seasonality=False,\n",
+ " daily_seasonality=False,\n",
+ " n_lags=3 * 24,\n",
+ " learning_rate=0.01,\n",
+ ")\n",
+ "\n",
+ "df_train, df_test = m.split_df(sf_pv_df, freq=\"H\", valid_p=0.10)\n",
+ "\n",
+ "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"bar\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 111
},
+ "id": "OnEPYrkscVtf",
+ "outputId": "fe0218f4-0fbe-4d74-86f3-0e74195681a7"
+ },
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "18c76ac000d94ad29e8667257ac474b5",
- "version_major": 2,
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- },
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- "Validation: 0it [00:00, ?it/s]"
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- },
- "metadata": {},
- "output_type": "display_data"
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- {
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- "Validation: 0it [00:00, ?it/s]"
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wEBERUafHwEJERESdHgMLERERdXoMLERERNTpMbAQERFRp8fAQkRERJ0eAwsRERF1egwsRERE1OkxsBAREVGnx8BCREREnR4DCxEREXV6DCxERETU6f1/3C81YyDwHFoAAAAASUVORK5CYII=",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "m = NeuralProphet(\n",
- " yearly_seasonality=3,\n",
- " weekly_seasonality=False,\n",
- " daily_seasonality=8,\n",
- " growth=\"off\",\n",
- " learning_rate=0.1,\n",
- ")\n",
- "df_train, df_test = m.split_df(sf_pv_df, freq=\"H\", valid_p=0.10)\n",
- "\n",
- "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"plot\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 111
- },
- "id": "OnEPYrkscVtf",
- "outputId": "fe0218f4-0fbe-4d74-86f3-0e74195681a7"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " MAE_val | \n",
- " RMSE_val | \n",
- " Loss_val | \n",
- " RegLoss_val | \n",
- " epoch | \n",
- " MAE | \n",
- " RMSE | \n",
- " Loss | \n",
- " RegLoss | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 108 | \n",
- " 131.56044 | \n",
- " 147.321808 | \n",
- " 0.01395 | \n",
- " 0.0 | \n",
- " 108 | \n",
- " 92.003754 | \n",
- " 117.907326 | \n",
- " 0.006691 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE \\\n",
- "108 131.56044 147.321808 0.01395 0.0 108 92.003754 \n",
- "\n",
- " RMSE Loss RegLoss \n",
- "108 117.907326 0.006691 0.0 "
- ]
- },
- "execution_count": 7,
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- "execution_count": 8,
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- "forecast = m.predict(sf_pv_df)\n",
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- "forecast = m.predict(df_test)\n",
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- "### 1-step ahead forecast with Auto-Regresseion\n",
- "In this second section, we will train a 1-step ahead forecaster on solar irradiance data (that can be a proxy for solar PV production). We can train this forecaster considering the privious 24 steps and disabling trend and seasonality.\n",
- "\n",
- "The paramters that we can modify are the number of lags and the AR sparsity."
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0aDU0NEiSli1bpjlz5ujee+/VvHnztG7dOm3btk0PP/ywJKm0tFRz5szRLbfcooKCAo0bN07PPfec/v3f/1333XffAO7qqUlOa2aWEAAA+ZdzYFmwYIEOHTqkFStWKBQKqbq6Wps2bUoNrN27d29GtWT27Nlau3atbr/9dt12222aOHGiNmzYoClTpqTarFu3TsuXL9e1116rI0eOaNy4cbrzzjt14403DsAuDozktGZOCQEAkH+Gbdun/TtwOBxWWVmZWltbVVpaOiiv8a/Pv6cf/+5Nfbm6UvdffdGgvAYAAJ8kubx/D4lZQqcDTgkBAOAeAkuWUivdMugWAIC8I7BkKb0OCxUWAADyjcCSJVa6BQDAPQSWLHlZ6RYAANcQWLLk42rNAAC4hsCSpeQsoSiDbgEAyDsCS5aosAAA4B4CS5bSFz+kwgIAQL4RWLKUvvghFRYAAPKNwJIlH7OEAABwDYElS1z8EAAA9xBYspS6lhCzhAAAyDsCS5a8LM0PAIBrCCxZSi3NzxgWAADyjsCSpdSgW2YJAQCQdwSWLHlZOA4AANcQWLLkY9AtAACuIbBkKVlhsWzJ4rQQAAB5RWDJUnKWkMQFEAEAyDcCS5Z8ZvpQMY4FAID8IrBkqWeFhcACAEB+EViylFzpVuKUEAAA+UZgyZJhGOnl+amwAACQVwSWHCRPC7HaLQAA+UVgyUFy4C2r3QIAkF8ElhykL4BIhQUAgHwisOQguXhclDEsAADkFYElB16W5wcAwBUElhykB91SYQEAIJ8ILDlIDbplDAsAAHlFYMlBatAts4QAAMgrAksOvExrBgDAFQSWHPiY1gwAgCsILDlgWjMAAO4gsOSAac0AALiDwJIDnyc5S4gKCwAA+URgyQEXPwQAwB0ElhwwSwgAAHcQWHLALCEAANxBYMkBs4QAAHBHvwLL6tWrNX78eAWDQdXU1Gjr1q0nbb9+/XpNmjRJwWBQU6dO1VNPPXVcmzfffFNXXnmlysrKVFRUpJkzZ2rv3r396d6g8TFLCAAAV+QcWB577DHV19dr5cqV2rFjh6ZNm6a5c+eqpaWl1/ZbtmzRNddco+uuu047d+7U/PnzNX/+fO3evTvV5t1339Vll12mSZMm6dlnn9Wrr76qO+64Q8FgsP97Ngi4+CEAAO4wbNvO6d23pqZGM2fO1IMPPihJsixLVVVVuummm3Trrbce137BggXq6OjQxo0bU/ddcsklqq6u1po1ayRJV199tXw+n/7jP/6jXzsRDodVVlam1tZWlZaW9msb2Vj++Gv65da9+m7dp7WsbuKgvQ4AAJ8Eubx/51Rh6e7u1vbt21VXV5fegGmqrq5OTU1NvT6nqakpo70kzZ07N9Xesiz97ne/06c//WnNnTtXo0aNUk1NjTZs2HDCfkQiEYXD4YxbPqQG3XJKCACAvMopsBw+fFjxeFzl5eUZ95eXlysUCvX6nFAodNL2LS0tam9v11133aUrrrhC//Vf/6WvfOUr+upXv6rnnnuu1202NDSorKwsdauqqsplN/otOa2ZU0IAAOSX67OErES14stf/rK++93vqrq6Wrfeequ+9KUvpU4Zfdzy5cvV2tqauu3bty8vfWVaMwAA7vDm0njkyJHyeDxqbm7OuL+5uVkVFRW9PqeiouKk7UeOHCmv16vJkydntDn//PP1wgsv9LrNQCCgQCCQS9cHhDd1SogKCwAA+ZRThcXv92v69OlqbGxM3WdZlhobG1VbW9vrc2prazPaS9LmzZtT7f1+v2bOnKk9e/ZktHn77bc1bty4XLo36NIr3VJhAQAgn3KqsEhSfX29Fi1apBkzZmjWrFlatWqVOjo6tHjxYknSwoULNXr0aDU0NEiSli1bpjlz5ujee+/VvHnztG7dOm3btk0PP/xwapu33HKLFixYoMsvv1x/8zd/o02bNum3v/2tnn322YHZywGSPiVEhQUAgHzKObAsWLBAhw4d0ooVKxQKhVRdXa1NmzalBtbu3btXppku3MyePVtr167V7bffrttuu00TJ07Uhg0bNGXKlFSbr3zlK1qzZo0aGhr0ne98R5/5zGf061//WpdddtkA7OLAYaVbAADckfM6LENRvtZh+dfn39OPf/emvlxdqfuvvmjQXgcAgE+CQVuH5ZPOl6iwcEoIAID8IrDkIL00P4NuAQDIJwJLDnypWUJUWAAAyCcCSw6osAAA4A4CSw68jGEBAMAVBJYc+EwufggAgBsILDnwmMlTQlRYAADIJwJLDlLTmqmwAACQVwSWHHhZmh8AAFcQWHKQvPghs4QAAMgvAksOUhc/ZB0WAADyisCSA6Y1AwDgDgLLycSj0jubpd2/lmxbXqY1AwDgCq/bHRjS4t3SL77ufD9xLhc/BADAJVRYTsZXKBke5/tImKX5AQBwCYHlZAxDCpQ430fauPghAAAuIbD0JVjqfO0Ksw4LAAAuIbD0JZAILJHW9CkhBt0CAJBXBJa+pAJL+pSQbUtxTgsBAJA3BJa+JMew9DglJDHwFgCAfCKw9CXYo8LiSR8uBt4CAJA/BJa+pGYJheUx0xWWGBUWAADyhsDSlx5jWLxmz1NCVFgAAMgXAktfekxrNgyD5fkBAHABgaUvqQpLWJJYiwUAABcQWPryscCSnNrMLCEAAPKHwNKXHkvzS+kKC+uwAACQPwSWvvQYwyJJXk+ywkJgAQAgXwgsfflYhcXHoFsAAPKOwNKX4wbdUmEBACDfCCx9SQaWaKcUjynocw5ZVzTuYqcAAPhkIbD0JXlKSJIiYRUHvJKktq6YSx0CAOCTh8DSF69f8gad7yNtKgn6JEntEQILAAD5QmDJRo9xLMVBp8LS3hV1sUMAAHyyEFiykTwt1BVWCaeEAADIOwJLNoLpCyCWJCssnBICACBvCCzZSK3FElZxwBnDEqbCAgBA3hBYstFjDAsVFgAA8o/Ako1gmfO1i0G3AAC4gcCSjR7L8zPoFgCA/COwZCPjlBDrsAAAkG8Elmz0qLAkTwlRYQEAIH/6FVhWr16t8ePHKxgMqqamRlu3bj1p+/Xr12vSpEkKBoOaOnWqnnrqqRO2vfHGG2UYhlatWtWfrg2O5LTmrvSg2zbGsAAAkDc5B5bHHntM9fX1WrlypXbs2KFp06Zp7ty5amlp6bX9li1bdM011+i6667Tzp07NX/+fM2fP1+7d+8+ru0TTzyhF198UZWVlbnvyWDqZQxLeyQm2+aKzQAA5EPOgeW+++7T9ddfr8WLF2vy5Mlas2aNCgsL9cgjj/Ta/v7779cVV1yhW265Reeff75+9KMf6eKLL9aDDz6Y0W7//v266aab9Itf/EI+n69/ezNYAolZQpHW1Ckhy5Y6u7liMwAA+ZBTYOnu7tb27dtVV1eX3oBpqq6uTk1NTb0+p6mpKaO9JM2dOzejvWVZ+uY3v6lbbrlFF1xwQZ/9iEQiCofDGbdB1aPCUuDzyGMakhh4CwBAvuQUWA4fPqx4PK7y8vKM+8vLyxUKhXp9TigU6rP93XffLa/Xq+985ztZ9aOhoUFlZWWpW1VVVS67kbseY1gMw1BxgHEsAADkk+uzhLZv3677779fjz76qAzDyOo5y5cvV2tra+q2b9++we1kjwqLbLvHwFsqLAAA5ENOgWXkyJHyeDxqbm7OuL+5uVkVFRW9PqeiouKk7Z9//nm1tLRo7Nix8nq98nq9+uCDD/S9731P48eP73WbgUBApaWlGbdBlVyHxYpKsa5UhYVTQgAA5EdOgcXv92v69OlqbGxM3WdZlhobG1VbW9vrc2prazPaS9LmzZtT7b/5zW/q1Vdf1a5du1K3yspK3XLLLfrDH/6Q6/4MDn+xpET1J2NqM4EFAIB88Ob6hPr6ei1atEgzZszQrFmztGrVKnV0dGjx4sWSpIULF2r06NFqaGiQJC1btkxz5szRvffeq3nz5mndunXatm2bHn74YUnSiBEjNGLEiIzX8Pl8qqio0Gc+85lT3b+BYZrOaaFI2JnanFztlsACAEBe5BxYFixYoEOHDmnFihUKhUKqrq7Wpk2bUgNr9+7dK9NMF25mz56ttWvX6vbbb9dtt92miRMnasOGDZoyZcrA7UU+BEoTgaU1dUoozKBbAADyIufAIklLly7V0qVLe33s2WefPe6+q666SldddVXW2//LX/7Sn24Nrp6LxwWdihBjWAAAyA/XZwmdNnpMbU4uHscpIQAA8oPAkq1eludn0C0AAPlBYMlWcmpzJJwedMspIQAA8oLAkq0eFRYG3QIAkF8ElmylxrC0ptZhocICAEB+EFiylTol1MagWwAA8ozAkq2eY1gCzhgWBt0CAJAfBJZsZazDwikhAADyicCSrd7WYYnEFLdsFzsFAMAnA4ElW71UWCSpo5sqCwAAg43Akq0eY1gCXo/8HufQMfAWAIDBR2DJVrDM+doVlqTUaSEG3gIAMPgILNlKnhLqbpMsq8fAWxaPAwBgsBFYspU8JSRJ3T1Xu6XCAgDAYCOwZMsbkExn/ZWMqc0EFgAABh2BJVuGkTm1OcAFEAEAyBcCSy56mdrcxgUQAQAYdASWXPRcnp9TQgAA5A2BJRc9AguDbgEAyB8CSy56jGEpCTKGBQCAfCGw5KLHGJZiTgkBAJA3BJZc9BzDkjgl1MbCcQAADDoCSy56mSVEhQUAgMFHYMlFxjosXEsIAIB8IbDkIlVhSQ+6bWPQLQAAg47AkotA4orNrMMCAEBeEVhy0cspoWPRuKJxy8VOAQBw5iOw5KKXac2S1MFpIQAABhWBJRc9pjX7PKaCPufwMfAWAIDBRWDJRY8Ki6T0wFsCCwAAg4rAkovkGJZYlxTrTi0ex/L8AAAMLgJLLvwl6e97jGNp62K1WwAABhOBJRcer+Qrcr6PtKanNlNhAQBgUBFYctVzplDilFCYMSwAAAwqAkuuCs5yvnZ+mBp0y+JxAAAMLgJLrkoqnK9toVSFpZ0rNgMAMKgILLkqrXS+hg+kxrAwrRkAgMFFYMlVqsJykOsJAQCQJwSWXJWc43xtC6k44IxhYdAtAACDi8CSq15OCTGGBQCAwUVgyVWqwnIwtXAc67AAADC4+hVYVq9erfHjxysYDKqmpkZbt249afv169dr0qRJCgaDmjp1qp566qnUY9FoVD/4wQ80depUFRUVqbKyUgsXLtSBAwf607XBl6ywtDerxDkjxKBbAAAGWc6B5bHHHlN9fb1WrlypHTt2aNq0aZo7d65aWlp6bb9lyxZdc801uu6667Rz507Nnz9f8+fP1+7duyVJnZ2d2rFjh+644w7t2LFDjz/+uPbs2aMrr7zy1PZssBSdLRkeybZ0lnVUEoNuAQAYbIZt23YuT6ipqdHMmTP14IMPSpIsy1JVVZVuuukm3Xrrrce1X7BggTo6OrRx48bUfZdccomqq6u1Zs2aXl/j5Zdf1qxZs/TBBx9o7NixffYpHA6rrKxMra2tKi0tzWV3+ufe86W2A2q5+vea9ehH8ntMvX3nFwb/dQEAOIPk8v6dU4Wlu7tb27dvV11dXXoDpqm6ujo1NTX1+pympqaM9pI0d+7cE7aXpNbWVhmGoWHDhvX6eCQSUTgczrjlVakzjqW4+5AkqTtuKRKL57cPAAB8guQUWA4fPqx4PK7y8vKM+8vLyxUKhXp9TigUyql9V1eXfvCDH+iaa645YdpqaGhQWVlZ6lZVVZXLbpy6xMDb4LHm1F2cFgIAYPAMqVlC0WhUf/d3fyfbtvXQQw+dsN3y5cvV2tqauu3bty+PvVRq4K3ZdlBFfo8kBt4CADCYvLk0HjlypDwej5qbmzPub25uVkVFRa/PqaioyKp9Mqx88MEHevrpp096LisQCCgQCOTS9YGVsdqtTx3dcaY2AwAwiHKqsPj9fk2fPl2NjY2p+yzLUmNjo2pra3t9Tm1tbUZ7Sdq8eXNG+2RYeeedd/THP/5RI0aMyKVb+VeSmNrcYy2WcBeLxwEAMFhyqrBIUn19vRYtWqQZM2Zo1qxZWrVqlTo6OrR48WJJ0sKFCzV69Gg1NDRIkpYtW6Y5c+bo3nvv1bx587Ru3Tpt27ZNDz/8sCQnrHz961/Xjh07tHHjRsXj8dT4luHDh8vv9w/Uvg6cxKBbhbmeEAAA+ZBzYFmwYIEOHTqkFStWKBQKqbq6Wps2bUoNrN27d69MM124mT17ttauXavbb79dt912myZOnKgNGzZoypQpkqT9+/frySeflCRVV1dnvNYzzzyjz372s/3ctUHUs8IyitVuAQAYbDmvwzIU5X0dlkib1DBGkvT9Tz+lX716VP/4+U9r6f+cOPivDQDAGWLQ1mFBQqBE8hdLkmYOj0iSdu1rdbNHAACc0Qgs/ZVYi+XCsmOSpF37juoMKFYBADAkEVj6KzHwdkIgLK9p6HB7RPuPHnO5UwAAnJkILP2VGHjr7wxp0jklkpwqCwAAGHgElv7qMbW5umqYJGnX3qOudQcAgDMZgaW/EmNY1HZA1VVnSaLCAgDAYCGw9FcqsIRSFZbX9rcqGrfc6xMAAGcoAkt/JS6AqPBBnTuySKVBryIxS3tCbe72CwCAMxCBpb+SFZb2kEzZmpaosuzktBAAAAOOwNJfxeWSYUpWTOo4pIsYeAsAwKAhsPSXxysVjXK+bzug6rHDJEm79n3kXp8AADhDEVhORUmF8zV8UNPGDJMkvXuoQ63Hou71CQCAMxCB5VSUpq/aPKI4oLHDCyVJr/71qHt9AgDgDERgORWpqc0HJYkF5AAAGCQEllPRY7VbqUdgYaYQAAADisByKkqSp4QOSFKPgbdcuRkAgIFEYDkVPQbdStLkc0rl8xj6sKNbf/2IKzcDADBQCCynosegW0kK+jyafE6pJBaQAwBgIBFYTkVy0G3XUSnqVFSS41g27NzPaSEAAAYIgeVUBMsknzOVWWFnHMvfzayS32Pq6bda9NPn33OxcwAAnDkILKfCMI6b2nxBZZnu+NvJkqS7N+3R1vePuNU7AADOGASWU1WSObVZkr5RM1Zfrq5U3LK1dO0OtbR1udQ5AADODASWU3X2Z5yv7z+busswDP3zV6Zq4qhitbRFtOyXuxSLW+70DwCAMwCB5VRN/brz9fXfSN2dqbuLAl499I2LVej3qOm9D3X7ht2KxOIudRIAgNMbgeVUVV0iDRsndbdJb/0u46HzRpXorq9dKEla9/I+zV+9RX9uaXOjlwAAnNYILKfKNKVp1zjfv/LL4x6+clql/t+iGRpe5NebB8P60gMv6Jdb9zLlGQCAHBBYBsK0Bc7X957JGHyb9Lnzy7Vp2f/QZeeNVFfU0vLHX9OStTvUeiya544CAHB6IrAMhOHnOqeGbEt67Ve9NhlVGtS///0sLf/CJHlNQ0+9FtKXHnher7AiLgAAfSKwDJRpVztfd/1SOsHpHtM09H/mfEq//vZsVQ0v0L4jx/T1NVv0/154n1NEAACcBIFloFzwFckTkA69KYVePWnTaVXDtPGm/6EvTq1QNG7rRxvf0PX/vk2H2yN56iwAAKcXAstAKRgmTfqi8/0r6/psXlbg0+r/fbF+NH+K/F5Tf3yzRZ//v3/Spt3Hj4EBAOCTjsAykJKzhV5bL8X7HlBrGIa+eck4/WbJpZpUUaIjHd268ec7dPO6nWrtZEAuAABJBJaB9KnPSUVnSx2HpN2PZ/20888p1ZNLL9OSv/mUTEPasOuA/tf/fU6P7/irLIuxLQAAEFgGkscrXZiY4vzEDdLPvy7t357VU/1eU7fMnaRff3u2zh1ZpJa2iOp/9Yq+vPq/9dJ7Hw5ipwEAGPoM+wyYnhIOh1VWVqbW1laVlpa625nuDun3P5B2rZXsxFL8E+dK/+N7UtUs5wrPfeiKxvWz//6LVj/zZ7VHYpKkz08u1z/8zXmqrho2iJ0HACB/cnn/JrAMliPvSX+6x1n91k5c+LDiQmnWDc71h3wFfW7icHtEq/74tta+tFfJM0MXjinTNy4ZpyunVSro8wziDgAAMLgILEPJh+9KL9wnvfafUqzLua/gLOniRVLN/5FKK/vcxDvNbXrouXe18dWD6o454aeswKcvTj1HX5xaoUvOHSGfh7N7AIDTC4FlKOo8Iu38D+nlf5WO7nXuM33S1Kuk2Uul8gv63MSRjm79ats+/fzFD/TXj46l7i8r8Ol/TS7XnE+frenjzlLlsL6rNwAAuI3AMpRZcentTdKWB6W9W9L3T5jjrJY76UtS8OT7ELdsbXn3sJ56LaT/ej2kDzu6Mx6vLAvq4nFnaeroMp17drHOPbtIY4cXUoUBAAwpBJbTxV+3SVv+RXrzt+lxLp6A9Om50pSvSZ/6n1mFl5f/ckR/eD2kl/9yRG8ebFO8l6nQXtPQ6LMKVFlWoHOGBTV6WIEqyoI6uzigkSUBnV0c0Ihivwp8HhlZDAweKmzb1rFoXAGvRx5zYPodt5xtFvn7dyxs21Znd1y2pEKfR+YA9QsAzjQEltPNR3+RXv2Vs+Dc4bfT95s+aVytNPHz0nl10shPS+bJB9p2RGJ65a9HteODj7SnuV3vHWrX+4c71Nkdz6orXtNQcdCr4oBzKwp4Vej3qMjvVWHAo4DXlMc05DWdrz6PKb/XVMBryu8x5fMYMk1DhmHIk3izj1mWumOWonFbsbglw3AWzTMMydCJ38yTWcGybUVjtrrjcUXjttq6otp/tEv7P+rUgaNdOhZ19s3vNVXo96jQ51FJ0KfSAq9Kgz6VBL2K287sq65oXJGoJdOUivxeFST2rTtuaf/RYzpw9JhCrV2KWba8pqFhhX6dVehTaYFP3THL2UYsrq6oJW9i/30e52tXNK5wV0zhY1HFEqHRMJzXKQp4UmHQ2W/JNAznZhoyDefftmxZlmTLCT6GYchjSh7DOabJY+3cPLJlqyMS17HuuDqjMUVjtgxD8phG4udkqNCf+BkGvAp4TR3tjOpwe0SH2yP6sKNbAa9Hw4t8Glbo1/BCv7weQ9G48/OKxp2fXcxyfnbJ/Sr0e1SY2K+gzyPZzs/JlhP4uqJxdXbHdSzq9M2ybZmJH6hpGPJ5DAV8zjEJ+kz5vR6ZPY6LDGVsU3KOgWkaqa+xuKVIzOlfd9ySbdvymM4xTf5uFvqd/hX6PTINQ+2RmHPriikSi6so4FVJ6vfdJ5/XOWZe05TXk/hqGs73HlO2bStu2YpZzlfLtmXbSvVRif7advrnV+DzKOh39jXgNdXZHVd7JKa2rqjau2KybOf3xEz8Xfg9pgr8nsQx9sjvNWVZ6WNh27asxLFJfjjxp/4OnfbJ38nkPhhG5vNicVuRmKVILK5IzJJl2fJ6TPlMQz6vs8/+1N+0s53E7qX2OXmMvR5DPtNUd9xSZ7dzfDu744pbdur3NOBztpXsjy9xPL2J39Pkz9RObNvq0Vfn+Dr/NhO/217TTB2v5H7FLVvJ3xZDRuKYGgP2QQaDg8ByurJtKfSatPs/pbd+J33458zHfYXS2ZOk8snSqAuksjFSSYVUXO7cfMETbNZWKNylfUecN+QDrcd08GiXDrZ26XB7RIfanDevSGJAL3AiQUVUpg41a7jbXQESQazvNr5EGPMmTovbHwuWqe+VDqDJICRlBmlPInA7wdAJYYYMpaN1ZmAyeoTvZAiTkh9YMgP6x3fF6PGNIaUqvsn+JF/DNJP7kt7+ifR8OPnh0Ql/xz+eapPYH7/H1H9+e/bJD3iOBj2wrF69Wj/5yU8UCoU0bdo0PfDAA5o1a9YJ269fv1533HGH/vKXv2jixIm6++679cUvfjH1uG3bWrlypX7605/q6NGjuvTSS/XQQw9p4sSJWfXnjAksH/fhu9I7m6V3/kv64L/Ts4xOxOOXvEHJG3BOLQVLnZV3i86Wikc5s5N8hZK/0PnqDSZ+S50/tq64oU6zRB1msVqNUrWqSO1RjzqjcXV0x9URiSkWi8kTbZcnEpYn2qaYJbWpUGG7SG12UN2W7fyhW1I88enI7018Ekt8spKcP4pAPKzS7hZFzCKFfSNlGV4lHxwWDWl8x2uacOw1+a0ufVRQpY8Kxqu1aLxiRRUaXeJRZbGhcwqlszydspvfktXyhjyH98jTuledxWP1YdkFOlh0vvYFPyO/1aWzI/s0vOsDlR3bq5gZ0IdFE3Uw+Ckd8I+XfAWqHFag0cOCqiz1qyxgqLXb0JHOqI52RtXWFZXfayrodT4tBxKfervjVqIaYanAa2p4vEXDj+xS0aFdMmJdihSM0rHgKLX7R6kjcLYihZWK+UpkKf3pMflJ3bJt5z8iq1u+WKe88U4pHpVtRaVYVJYVU0xedSqgTjuoTjsgy/Sp0CcV+00V+CS/N6CoJ5DaZjRu61h3XB3dMUU6O+TpbFFhUYmKzxqlEaVFGl7kVyQW19HOqI60R9Ta0aGo7ZHP63U+XXsNeQ1DZx/dpaoPHlf5vt/LG+tQuOwz+ss5X9TbZ39eh73lzidfWSrs/lDF3S0yAyUyi0fIWzhcgUBAHtPo8cnZ+YSfrFRFu9oVi0bV7SlKvXEkKzI9z8jZthKVDUvxuC1f4lN7wOdRwGNKhmRZtuK2LctyKgjJSk9nosozwnNMn4q8oXEdr6okElKoaLLeLb5IH3jGqb3bUiwWV0nXQY3rfE2Vx97WUXOY3vNO1Lu+8xS2i1LVK6cyYGb8R5+sFpqmZNq2gnaHCuId6oxJHVGpPWaoM2bI8BeqqCCokkQV0+NJVxbillPVSlanOrvjisat9JuKlKpemqbkU1yFVruO2kXqihsZFadY3FbMciplktJvoJK8HsOpfHidyozHNFIV0JhlJ6pqiSpbYnvJN65kwSKe+DnGepyCLkpU8ooCXhmG1B1LV8EisXhi/077z8ifWH6vqbd//IUB3eagBpbHHntMCxcu1Jo1a1RTU6NVq1Zp/fr12rNnj0aNGnVc+y1btujyyy9XQ0ODvvSlL2nt2rW6++67tWPHDk2ZMkWSdPfdd6uhoUH/9m//pgkTJuiOO+7Qa6+9pjfeeEPBYO9Vg/7u8GnLijtruzS/7twOvSW1HZTam6W2Zik+iFd6NkzJ8Dino04WmgxTCpQ6t2CpFCiR/EWS6U08P/ExoHWf9NEHUtfRzOeWVDrTvFv/KrUdGLz96a3fBcOlWESKHZOsWPIBJ9T5gpK3wNkPM3ksvImAmAiJpkc6tMf5efTFVySVjZYKR0jRY1K001lwsLtD6m7v8fr9FCh1AmpxhRQodn5PWvdLnYePbxcc5rxeNPH6VszZt+IKp3pXUuH8rn282tdT+RQp0iaF9/fe9+AwyV/sBOlkoI5FpGMfSceOpH+nDFMKljnBOlDq/O74Cp01izx+qfND57IX7c3O95Jz2tTjd1aZNj52utTjc17XX+T8Lh77yPnbOe5zrJyf/zkXOj/DthNcgHT4p5zwb1vOopBWPPG97WzTttL71XU0PS6tN/5ip0+BUqf/RmL/DVOKdad/Ht2dTvvC4c5xKRzhtG8PSeGDzvGQ7fzMSiulsiqp5BwnncQiUry7x9cuZ9uxLud1fAXp4+sNOPdJiY/UpnNsTW/62NpxKR6TrKhzrbTudqkrLDsSlrrCkm3JSLZP/X0E039DnoBkemSbHtnyyDYM2VZcdjwm24rJtuLOa3r9MjwByeOXEeuQcewjmV1HneMqyfYXyfYWyfIVyPYEZNiWDDk/B8MwZHsDsj3O75nt8Tsh2bJkW1aP6oOzj4ZhOM+14jJs5+dpWDEZtvNvI/Ez7vkbY8uQZfoUN/2KGz7FDW9qG6YVc9qbHlmmT7bpU9zwJV7HTkRau8e2Er86tp1KvkbGayW/SdRvUh8wlUj28fTvomFIpld28v9rw0xuvMfWnMBqG+m77PRLZPh49cqWLRleVf3v+0/8e90PgxpYampqNHPmTD344IOSJMuyVFVVpZtuukm33nrrce0XLFigjo4Obdy4MXXfJZdcourqaq1Zs0a2bauyslLf+9739I//+I+SpNbWVpWXl+vRRx/V1VdfPaA7fEaybamr1fkPLtaVfuPtapXaD0kdLc5/bMc+SrxBHku0jSj9C207bzbHjmb3H26ygiM5z7H6ebHGgrOkSPvxzze90jnV0thLnDYfvuuM7/nwHWe/ZCT+ow06//mPPE8aNdk5ZXbWOOnwO9KBHdL+Hc4brjcojfiUNGKiNOI8JySEXpOad6ff/AaC6ZUqpkpjZjlX8G476LyxtB103tAT/+lmxZv+T14en/OfkBV1fnbRzv71zxPoX7j1FUkXzJeqr5VGne8MFN/9n9L7zysjAJhe5/RktDO3fc2n4edKVZdIw8ZKf31Z2vuiExCSTK90zjRp9HSpvUU6sFM6+kH/XsvjT7yxcDFTnAE8AemOlgHdZC7v395cNtzd3a3t27dr+fLlqftM01RdXZ2ampp6fU5TU5Pq6+sz7ps7d642bNggSXr//fcVCoVUV1eXerysrEw1NTVqamrqNbBEIhFFIun/dMPhcC67ceYxDOfNsWDYwG3TsqRI2Pkklfo0GXcqDcFS5xNZkm07Qamr1blF2pznRtrSn9qTz7dtqXS0EyqGjXMqAJblhKrWvzq3wuHS6BnOqauPs+10FeBkM3gmXC7pOuf7WCTxabGXad22nf7Envo0WJD4pJsIftGuROUl+ck6sT+pT68R5ziVjXFCVm/9TurulMIHpPBfnTd0X2G6kuAvSnzyLnYCguckf56W5fQpHnUCTfJTVazLCadtIWe/Im3OJ+6yMc4tWOb0vas1UeH4yHkdf3H6dGF3p/P8tkTQCpZJn/mCUxFImr7IuYUPOm/6RWdLwxKf7pMDw+MxJ/h2fpgOyPGIczw9vnTVoGC48/PsanXaHzvqfB/tTFegYhGnulB8tlQ0Sioa6exzvNs5BvGojqucxLudMNzdIXW3Of/ZjpkplZR/rF1UOrBLan7NGdheefHxP8POI9LBXc7xTFYbk59ikxUJw3ACSsFZ6VvPvxPLcvrU3e78fXSFna+JT+XO+bC4sw1/UfpmW1LnR85xTFakiiuk0nOcqmTBMCdYtf7VqV62HZRTHQykTw97A5n/lu38nJPHONaljA8vyb+zeDT9+24mK4uJyou/OFFJTVRUTW+ifTxRhelO/O10pT9Ipf5+LGe/kts0vc4xtKLpKlA84rxGwfD08TQM5/glK5KxSI/ffzNd5Uq9Zne6eqWPD9RIVMV6/jxT/Un+25uuPCXZiZ9jz+pVch+Sz7ViPdp0K1ndSFZIsrlES+Zr2unf7+T3qcp34qts57hascT/tVbvr/mxistJXvT4u8ycIsOAy+nVDx8+rHg8rvLyzD/48vJyvfXWW70+JxQK9do+FAqlHk/ed6I2H9fQ0KB/+qd/yqXryJVpZh+AjES1w1fgnELoz2slTz+MmdH3a3l8uW2/55tGb9tLvnY++AudatDI805tO6bpvJl9nC/o/NxGnmT8l8crFY1wbr0pOMs5ZZWN0nOkyVee5HVGOrds+Aud7eWbxydVzXRuJ1I43Flm4FSYpmQmTo9ke0yS+hrjXDY68TOr6W/vgCHvtFxJbPny5WptbU3d9u3b53aXAADAIMopsIwcOVIej0fNzZkDC5ubm1VR0fsn1IqKipO2T37NZZuBQEClpaUZNwAAcObKKbD4/X5Nnz5djY2Nqfssy1JjY6Nqa2t7fU5tbW1Ge0navHlzqv2ECRNUUVGR0SYcDuull1464TYBAMAnS84jaOrr67Vo0SLNmDFDs2bN0qpVq9TR0aHFixdLkhYuXKjRo0eroaFBkrRs2TLNmTNH9957r+bNm6d169Zp27ZtevjhhyU5awrcfPPN+vGPf6yJEyempjVXVlZq/vz5A7enAADgtJVzYFmwYIEOHTqkFStWKBQKqbq6Wps2bUoNmt27d6/MHrMxZs+erbVr1+r222/XbbfdpokTJ2rDhg2pNVgk6fvf/746Ojp0ww036OjRo7rsssu0adOmrNZgAQAAZz6W5gcAAK7I5f37tJwlBAAAPlkILAAAYMgjsAAAgCGPwAIAAIY8AgsAABjyCCwAAGDII7AAAIAhz91rRQ+Q5FIy4XDY5Z4AAIBsJd+3s1kS7owILG1tbZKkqqoql3sCAABy1dbWprKyspO2OSNWurUsSwcOHFBJSYkMwxjQbYfDYVVVVWnfvn2sojsAOJ4Dh2M5sDieA4djObDO5ONp27ba2tpUWVmZcVmf3pwRFRbTNDVmzJhBfY3S0tIz7hfFTRzPgcOxHFgcz4HDsRxYZ+rx7KuyksSgWwAAMOQRWAAAwJBHYOlDIBDQypUrFQgE3O7KGYHjOXA4lgOL4zlwOJYDi+PpOCMG3QIAgDMbFRYAADDkEVgAAMCQR2ABAABDHoEFAAAMeQSWPqxevVrjx49XMBhUTU2Ntm7d6naXhryGhgbNnDlTJSUlGjVqlObPn689e/ZktOnq6tKSJUs0YsQIFRcX62tf+5qam5td6vHp46677pJhGLr55ptT93Esc7N//3594xvf0IgRI1RQUKCpU6dq27Ztqcdt29aKFSt0zjnnqKCgQHV1dXrnnXdc7PHQFI/Hdccdd2jChAkqKCjQpz71Kf3oRz/KuCYMx/LE/vSnP+lv//ZvVVlZKcMwtGHDhozHszl2R44c0bXXXqvS0lINGzZM1113ndrb2/O4F3lm44TWrVtn+/1++5FHHrFff/11+/rrr7eHDRtmNzc3u921IW3u3Ln2z372M3v37t32rl277C9+8Yv22LFj7fb29lSbG2+80a6qqrIbGxvtbdu22Zdccok9e/ZsF3s99G3dutUeP368feGFF9rLli1L3c+xzN6RI0fscePG2d/61rfsl156yX7vvffsP/zhD/af//znVJu77rrLLisrszds2GC/8sor9pVXXmlPmDDBPnbsmIs9H3ruvPNOe8SIEfbGjRvt999/316/fr1dXFxs33///ak2HMsTe+qpp+wf/vCH9uOPP25Lsp944omMx7M5dldccYU9bdo0+8UXX7Sff/55+7zzzrOvueaaPO9J/hBYTmLWrFn2kiVLUv+Ox+N2ZWWl3dDQ4GKvTj8tLS22JPu5556zbdu2jx49avt8Pnv9+vWpNm+++aYtyW5qanKrm0NaW1ubPXHiRHvz5s32nDlzUoGFY5mbH/zgB/Zll112wscty7IrKirsn/zkJ6n7jh49agcCAfuXv/xlPrp42pg3b57993//9xn3ffWrX7WvvfZa27Y5lrn4eGDJ5ti98cYbtiT75ZdfTrX5/e9/bxuGYe/fvz9vfc8nTgmdQHd3t7Zv3666urrUfaZpqq6uTk1NTS727PTT2toqSRo+fLgkafv27YpGoxnHdtKkSRo7dizH9gSWLFmiefPmZRwziWOZqyeffFIzZszQVVddpVGjRumiiy7ST3/609Tj77//vkKhUMbxLCsrU01NDcfzY2bPnq3Gxka9/fbbkqRXXnlFL7zwgr7whS9I4lieimyOXVNTk4YNG6YZM2ak2tTV1ck0Tb300kt573M+nBEXPxwMhw8fVjweV3l5ecb95eXleuutt1zq1enHsizdfPPNuvTSSzVlyhRJUigUkt/v17BhwzLalpeXKxQKudDLoW3dunXasWOHXn755eMe41jm5r333tNDDz2k+vp63XbbbXr55Zf1ne98R36/X4sWLUods97+7jmemW699VaFw2FNmjRJHo9H8Xhcd955p6699lpJ4liegmyOXSgU0qhRozIe93q9Gj58+Bl7fAksGFRLlizR7t279cILL7jdldPSvn37tGzZMm3evFnBYNDt7pz2LMvSjBkz9M///M+SpIsuuki7d+/WmjVrtGjRIpd7d3r51a9+pV/84hdau3atLrjgAu3atUs333yzKisrOZYYFJwSOoGRI0fK4/EcN9uiublZFRUVLvXq9LJ06VJt3LhRzzzzjMaMGZO6v6KiQt3d3Tp69GhGe47t8bZv366WlhZdfPHF8nq98nq9eu655/Qv//Iv8nq9Ki8v51jm4JxzztHkyZMz7jv//PO1d+9eSUodM/7u+3bLLbfo1ltv1dVXX62pU6fqm9/8pr773e+qoaFBEsfyVGRz7CoqKtTS0pLxeCwW05EjR87Y40tgOQG/36/p06ersbExdZ9lWWpsbFRtba2LPRv6bNvW0qVL9cQTT+jpp5/WhAkTMh6fPn26fD5fxrHds2eP9u7dy7H9mM997nN67bXXtGvXrtRtxowZuvbaa1Pfcyyzd+mllx43xf7tt9/WuHHjJEkTJkxQRUVFxvEMh8N66aWXOJ4f09nZKdPMfAvxeDyyLEsSx/JUZHPsamtrdfToUW3fvj3V5umnn5ZlWaqpqcl7n/PC7VG/Q9m6devsQCBgP/roo/Ybb7xh33DDDfawYcPsUCjkdteGtG9/+9t2WVmZ/eyzz9oHDx5M3To7O1NtbrzxRnvs2LH2008/bW/bts2ura21a2trXez16aPnLCHb5ljmYuvWrbbX67XvvPNO+5133rF/8Ytf2IWFhfbPf/7zVJu77rrLHjZsmP2b3/zGfvXVV+0vf/nLTMXtxaJFi+zRo0enpjU//vjj9siRI+3vf//7qTYcyxNra2uzd+7cae/cudOWZN933332zp077Q8++MC27eyO3RVXXGFfdNFF9ksvvWS/8MIL9sSJE5nW/En2wAMP2GPHjrX9fr89a9Ys+8UXX3S7S0OepF5vP/vZz1Jtjh07Zv/DP/yDfdZZZ9mFhYX2V77yFfvgwYPudfo08vHAwrHMzW9/+1t7ypQpdiAQsCdNmmQ//PDDGY9blmXfcccddnl5uR0IBOzPfe5z9p49e1zq7dAVDoftZcuW2WPHjrWDwaB97rnn2j/84Q/tSCSSasOxPLFnnnmm1/8nFy1aZNt2dsfuww8/tK+55hq7uLjYLi0ttRcvXmy3tbW5sDf5Ydh2j2UJAQAAhiDGsAAAgCGPwAIAAIY8AgsAABjyCCwAAGDII7AAAIAhj8ACAACGPAILAAAY8ggsAABgyCOwAACAIY/AAgAAhjwCCwAAGPIILAAAYMj7/76jDHqSKs8oAAAAAElFTkSuQmCC",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "m = NeuralProphet(\n",
- " growth=\"off\",\n",
- " yearly_seasonality=False,\n",
- " weekly_seasonality=False,\n",
- " daily_seasonality=False,\n",
- " n_lags=3 * 24,\n",
- " learning_rate=0.01,\n",
- ")\n",
- "\n",
- "df_train, df_test = m.split_df(sf_pv_df, freq=\"H\", valid_p=0.10)\n",
- "\n",
- "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"plot\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 111
- },
- "id": "OnEPYrkscVtf",
- "outputId": "fe0218f4-0fbe-4d74-86f3-0e74195681a7"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " MAE_val | \n",
- " RMSE_val | \n",
- " Loss_val | \n",
- " RegLoss_val | \n",
- " epoch | \n",
- " MAE | \n",
- " RMSE | \n",
- " Loss | \n",
- " RegLoss | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 108 | \n",
- " 18.157923 | \n",
- " 30.523888 | \n",
- " 0.000599 | \n",
- " 0.0 | \n",
- " 108 | \n",
- " 30.129263 | \n",
- " 52.539661 | \n",
- " 0.001441 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
- "108 18.157923 30.523888 0.000599 0.0 108 30.129263 52.539661 \n",
- "\n",
- " Loss RegLoss \n",
- "108 0.001441 0.0 "
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metrics.tail(1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9ce3453e396b464d948933eeb13f7b24",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Predicting: 245it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "forecast = m.predict(sf_pv_df)\n",
- "fig = m.plot(forecast)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 440
- },
- "id": "5v-4bpNUvELW",
- "outputId": "089e3649-a238-4ec1-9593-32c2844d0ee4"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "3bf96f52212b4dfc8abb3cac2db993b8",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Predicting: 245it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "forecast = m.predict(df_test)\n",
- "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
- "fig = m.plot(forecast)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 871
- },
- "id": "0RrB1J5QcZ1u",
- "outputId": "acc2e734-f506-4ef7-adfb-ab18076b66e1",
- "tags": []
- },
- "outputs": [],
- "source": [
- "fig_comp = m.plot_components(forecast)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000
- },
- "id": "G0E4dLGxcbsO",
- "outputId": "9590ed3b-6e76-4fdd-d4bf-c310c04ac7ec",
- "tags": []
- },
- "outputs": [],
- "source": [
- "fig_param = m.plot_parameters()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 440
- },
- "id": "pmx8KTT0cgyR",
- "outputId": "16062765-4e93-43c6-c434-857ad4b19008"
- },
- "outputs": [],
- "source": [
- "fig_prediction = m.plot(forecast[-48:])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Sparsifying the AR coefficients\n",
- "By setting an `ar_reg > 0` we can reduce the number of non-zero AR coefficients."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 148,
- "referenced_widgets": [
- "4ac0917121f8498698e087259b787dcf",
- "94108fe9090f47c7ba2216479e0d3fac",
- "2d8235496ec642af8192f52d9f2692b1",
- "c94a8ae41b994c55a96ad44806b0f1c7",
- "24bf564f55644476911a6cf004a395e7",
- "87c170d1e00742a29e7f797e98c49cc2",
- "8a192ccc35e94e9f8be85898ed583e2c",
- "9467345334da47a8beadc770feef952a",
- "dc468cd35d2b4f0e8eb287689ac15412",
- "f35fc9cbd82c4187a4cdc08c3ac26998",
- "aab682cd3df24821a80331720f7c24e5"
- ]
- },
- "id": "s7faUgnrvGFN",
- "outputId": "50da2450-767f-4e3b-f03d-d03226d24ff8"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "8f472fe0bfb64163afcdc59aa5a237e9",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Training: 0it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9b82688cc6c54664ae288a584fcfac04",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "3b159bd3c8ae47929d06ce7f18645823",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6aecca4944ec4db38cccbac4e1df6c27",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
- ]
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",
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- ""
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- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "m = NeuralProphet(\n",
- " growth=\"off\",\n",
- " yearly_seasonality=False,\n",
- " weekly_seasonality=False,\n",
- " daily_seasonality=False,\n",
- " n_lags=3 * 24,\n",
- " ar_reg=1,\n",
- " learning_rate=0.01,\n",
- ")\n",
- "\n",
- "df_train, df_test = m.split_df(sf_pv_df, freq=\"H\", valid_p=0.10)\n",
- "\n",
- "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"plot\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " MAE_val | \n",
- " RMSE_val | \n",
- " Loss_val | \n",
- " RegLoss_val | \n",
- " epoch | \n",
- " MAE | \n",
- " RMSE | \n",
- " Loss | \n",
- " RegLoss | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 108 | \n",
- " 17.391836 | \n",
- " 31.084551 | \n",
- " 0.00119 | \n",
- " 0.000569 | \n",
- " 108 | \n",
- " 30.613697 | \n",
- " 53.866795 | \n",
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- " 0.000568 | \n",
- "
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- " \n",
- "
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- "
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- ],
- "text/plain": [
- " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
- "108 17.391836 31.084551 0.00119 0.000569 108 30.613697 53.866795 \n",
- "\n",
- " Loss RegLoss \n",
- "108 0.002079 0.000568 "
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metrics.tail(1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000
- },
- "id": "G0E4dLGxcbsO",
- "outputId": "9590ed3b-6e76-4fdd-d4bf-c310c04ac7ec",
- "tags": []
- },
- "outputs": [],
- "source": [
- "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
- "fig_param = m.plot_parameters()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1-step ahead forecast with Auto-Regression including Integration\n",
- "Next, we will add the differences of the series as a lagged covariate.\n",
- "This basically extends the model from AR to ARI, where the I stands for 'integrated' time series."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " ds | \n",
- " y | \n",
- " I | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 8757 | \n",
- " 2015-12-31 22:00:00 | \n",
- " 0 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " 8758 | \n",
- " 2015-12-31 23:00:00 | \n",
- " 0 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " 8759 | \n",
- " 2016-01-01 00:00:00 | \n",
- " 0 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- "
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- "
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- ],
- "text/plain": [
- " ds y I\n",
- "8757 2015-12-31 22:00:00 0 0\n",
- "8758 2015-12-31 23:00:00 0 0\n",
- "8759 2016-01-01 00:00:00 0 0"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = sf_pv_df.copy(deep=True)\n",
- "df[\"I\"] = np.append(0, sf_pv_df[\"y\"].values[1:] - sf_pv_df[\"y\"].values[:-1])\n",
- "df.tail(3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
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- "text/plain": [
- "Training: 0it [00:00, ?it/s]"
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oDwAAbxBGUjLzjBBGAADIKcJICjUjAAB4gzCSEmQGVgAAPEEYSUkfpnGMlHQoYgUAIFcIIykB/0BXcKgGAIDcIYykpGtGJIpYAQDIpXGFkY0bN2ru3LkKh8Oqra3Vtm3bRl33gQceUE1NjcrKyjRlyhQtXrxYd91117gbPFkC9qCREepGAADImazDyJYtW9TY2Kj169dr+/btWrRokZYvX66DBw+OuP60adN0/fXXq6WlRX/84x/V0NCghoYGPfrooyfc+Ilk25b8dvqMGmpGAADIlazDyK233qrVq1eroaFBCxYs0KZNm1RYWKjNmzePuP4HPvABffrTn9b555+vc845R2vWrNGFF16oJ5988oQbP9G4WB4AALmXVRiJx+NqbW1VfX39wA5sW/X19WppaTnu9sYYNTc3a+fOnXr/+98/6nqxWEzRaHTILRfSdSPUjAAAkDtZhZH29nYlk0lVVlYOWV5ZWalIJDLqdp2dnSoqKlIwGNQVV1yh2267TR/+8IdHXb+pqUmlpaWZW3V1dTbNHLeg3yeJkREAAHIpJ2fTFBcXa8eOHXrmmWd04403qrGxUU888cSo669du1adnZ2Z2969e3PRTAXTs7BysTwAAHLGn83K5eXl8vl8amtrG7K8ra1NVVVVo25n27bOPfdcSdLixYv18ssvq6mpSR/4wAdGXD8UCikUCmXTtAmRnmuEwzQAAOROViMjwWBQS5cuVXNzc2aZ4zhqbm5WXV3dmPfjOI5isVg2T50TFLACAJB7WY2MSFJjY6NWrVqlmpoaLVu2TBs2bFBPT48aGhokSStXrtSsWbPU1NQkya3/qKmp0TnnnKNYLKZHHnlEd911l26//faJfSUTgDACAEDuZR1GVqxYoUOHDmndunWKRCJavHixtm7dmilq3bNnj+xBE4j19PToa1/7mt566y0VFBRo/vz5uvvuu7VixYqJexUTJF0zwsXyAADIHcsYc9JXa0ajUZWWlqqzs1MlJSWT9jyfu/0pPbv7HW36wnt0+cIZk/Y8AADkg7F+f3NtmkHSh2nizMAKAEDOEEYGSZ9Nw7VpAADIHcLIIJl5RihgBQAgZwgjg3A2DQAAuUcYGYSaEQAAco8wMggjIwAA5B5hZJBQwO2Ovv6kxy0BACB/EEYGKQ67c8B19yU8bgkAAPmDMDJISTggSYr29XvcEgAA8gdhZJCSglQY6WVkBACAXCGMDFKSOkzDyAgAALlDGBkkMzJCGAEAIGcII4NkakY4TAMAQM4QRgYpLeAwDQAAuUYYGWRgZKRfxjALKwAAuUAYGSRdM+IYqSfOxGcAAOQCYWSQkN9WMDUlfLSXQzUAAOQCYWQQy7JUkqob6SSMAACQE4SRowyuGwEAAJOPMHKU4sxcI5zeCwBALhBGjpKZhZWREQAAciK/w8iDV0v/+xJp91OZRczCCgBAbuV3GHnnTengi1JPe2YRs7ACAJBb+R1GglPcn/GezKISZmEFACCnCCOSFO/OLOJsGgAAcivPw0iR+3PIyAg1IwAA5FKeh5ERDtNkzqahZgQAgFwgjEiMjAAA4CHCiDRyzQhhBACAnMjzMDK8ZqS0gFN7AQDIpTwPI6Of2tvV1y/HMV60CgCAvEIYkUY8TOMYqSfO6AgAAJMtz8PI8MM04YBPQb/bLVwsDwCAyZfnYWT4YRqJic8AAMglwog0PIwUcOVeAAByJc/DSPowTfeQxQOn93KYBgCAyZbnYWS0kREO0wAAkCuEEUly+qVEPLM4MyU8E58BADDp8juMBKYM3B98ei8TnwEAkDP5HUZ8fskfdu8PuVgeU8IDAJAr+R1GpGPOwtpJzQgAAJOOMDJSGGGeEQAAcoYwMsLpvZmaEQ7TAAAw6QgjI46MpCc9o4AVAIDJRhgZsWaEkREAAHKFMDLSYRpqRgAAyBnCyDHOpumKJeQ4xotWAQCQN8YVRjZu3Ki5c+cqHA6rtrZW27ZtG3XdO++8U+973/s0depUTZ06VfX19cdcP+eOcTaNMVJ3nLoRAAAmU9ZhZMuWLWpsbNT69eu1fft2LVq0SMuXL9fBgwdHXP+JJ57QlVdeqccff1wtLS2qrq7WRz7yEe3bt++EGz8hMmFk4DBNOOBT0O92DYdqAACYXFmHkVtvvVWrV69WQ0ODFixYoE2bNqmwsFCbN28ecf177rlHX/va17R48WLNnz9f//Zv/ybHcdTc3HzCjZ8QmZqRoy6WF2ZKeAAAciGrMBKPx9Xa2qr6+vqBHdi26uvr1dLSMqZ9HDlyRP39/Zo2bdqo68RiMUWj0SG3STPqlXu5WB4AALmQVRhpb29XMplUZWXlkOWVlZWKRCJj2se3vvUtzZw5c0igOVpTU5NKS0szt+rq6myamZ0RDtNInFEDAECu5PRsmptuukn33XefHnzwQYXD4VHXW7t2rTo7OzO3vXv3Tl6jRjlMU5qZa4TDNAAATCZ/NiuXl5fL5/Opra1tyPK2tjZVVVUdc9tbbrlFN910k37961/rwgsvPOa6oVBIoVAom6aN36iHaRgZAQAgF7IaGQkGg1q6dOmQ4tN0MWpdXd2o2/3TP/2Tvve972nr1q2qqakZf2snw2hhJEzNCAAAuZDVyIgkNTY2atWqVaqpqdGyZcu0YcMG9fT0qKGhQZK0cuVKzZo1S01NTZKkH/zgB1q3bp3uvfdezZ07N1NbUlRUpKKiogl8KeM0wgys0uCREQ7TAAAwmbIOIytWrNChQ4e0bt06RSIRLV68WFu3bs0Ute7Zs0e2PTDgcvvttysej+tzn/vckP2sX79e//AP/3BirZ8Io46McH0aAAByIeswIknXXnutrr322hEfe+KJJ4b8e9euXeN5itw53qm91IwAADCpuDZN+jBNoldykpnFjIwAAJAbhJH0yIh01MXyqBkBACAXCCO+oGSnjlYNuVgeZ9MAAJALhBHLGvnKvcwzAgBAThBGpBFP703XjHTFEnIc40WrAADIC4QRacSRkeLUYRpj3EACAAAmB2FEGjGMhAM+hfxu93CoBgCAyUMYkY4/CytFrAAATBrCiHT869Nwei8AAJOGMCId/8q9jIwAADBpCCPSoDBy1GGaMKf3AgAw2Qgj0qCakdFGRjhMAwDAZCGMSGOoGWFkBACAyUIYkagZAQDAQ4QRafRTe1M1I52MjAAAMGkII9KoIyMzy8KSpD8d7D56CwAAMEEII9KoYWTpnKmSpBf3R3UkThErAACTgTAijXpq76yyAlWVhJVwjHbs7ch9uwAAyAOEEWnUU3sty1LNXHd0pHXXO7luFQAAeYEwIo16mEaSalKHap7ZTRgBAGAyEEakY4eRudMkSc/tfkdJx+SyVQAA5AXCiDT01F4zNHDMryrWlKBPXbGEdka6PGgcAACnN8KINDAyIiP19w55yO+z9Z7UoZrW3Ydz3DAAAE5/hBFJ8hdIstz7IxyqSZ/i+wxFrAAATDjCiCTZ9qin90rSRam6kVaKWAEAmHCEkbRjFLEuri6Tz7a0r6NX+zp6hz0OAADGjzCSdowwMiXk14IZJZKkZ3dRNwIAwEQijKQd4zCNpIHJzzhUAwDAhCKMpI0yC2tazRy3boQiVgAAJhZhJO0Yh2mkgZGRnZGoon39uWoVAACnPcJI2nEO01SWhFU9rUCOkZ7b05G7dgEAcJojjKQd5zCNJF2UOlTTShErAAAThjCSdpzDNJK0dC6TnwEAMNEII2ljCCPvme2GkRf2dcrhonkAAEwIwkjacWpGJOnciiIF/ba6YgntOXwkRw0DAOD0RhhJG0PNSMBn6/yqYknSC/s7c9EqAABOe4SRtDEcppGkC2aVSpJe3B+d7BYBAJAXCCNpYzhMI0kXzHSnhX9hHyMjAABMBMJI2hgO00jSwpkDIyPGUMQKAMCJIoykjfEwzXlVxfLZlg73xHWgsy8HDQMA4PRGGEkbYxgJB3yaV+GOolA3AgDAiSOMpGUO03Qdd9ULUodqqBsBAODEEUbSBo+MHKcWZOEst4j1RU7vBQDghBFG0tJhxElIyfgxV104Kz0ywmEaAABOFGEkLTBl4P5x6kbOn1Eiy5Ii0T61d8cmuWEAAJzeCCNpPr/kD7v3jzPXSFHIr7PK3fBCESsAACeGMDLYGM+okShiBQBgoowrjGzcuFFz585VOBxWbW2ttm3bNuq6L774oj772c9q7ty5sixLGzZsGG9bJ18WYWThTIpYAQCYCFmHkS1btqixsVHr16/X9u3btWjRIi1fvlwHDx4ccf0jR47o7LPP1k033aSqqqoTbvCkypzee+zDNNJAESuHaQAAODFZh5Fbb71Vq1evVkNDgxYsWKBNmzapsLBQmzdvHnH9iy66SDfffLM+//nPKxQKnXCDJ1VWh2nckZHdbx9RZ2//ZLYKAIDTWlZhJB6Pq7W1VfX19QM7sG3V19erpaVlwhoVi8UUjUaH3HIiizBSVhjUrLICSdJLjI4AADBuWYWR9vZ2JZNJVVZWDlleWVmpSCQyYY1qampSaWlp5lZdXT1h+z6mLA7TSEx+BgDARDgpz6ZZu3atOjs7M7e9e/fm5omzGBmRhl7BFwAAjI8/m5XLy8vl8/nU1tY2ZHlbW9uEFqeGQiFv6kumTHd/tr86ptUHZmJlZAQAgPHKamQkGAxq6dKlam5uzixzHEfNzc2qq6ub8Mbl3Dl/5v589b8lxznu6uki1j8d6lZvPDmZLQMA4LSV9WGaxsZG3XnnnfrpT3+ql19+WVdffbV6enrU0NAgSVq5cqXWrl2bWT8ej2vHjh3asWOH4vG49u3bpx07duj111+fuFcxUea+160b6Y5IB3Ycd/XpxSGdMSUox0ivth3/ar8AAGC4rMPIihUrdMstt2jdunVavHixduzYoa1bt2aKWvfs2aMDBw5k1t+/f7+WLFmiJUuW6MCBA7rlllu0ZMkSfeUrX5m4VzFR/KFBoyNbj7u6ZVk6f4Y7OvJKhLoRAADGI6uakbRrr71W11577YiPPfHEE0P+PXfuXBljxvM03njX5dLLv5B2/kr64LePu/r8qmI9+Xq7Xj7AyAgAAONxUp5N46l5H5FkSZE/StH9x109PTLy8gFGRgAAGA/CyNGKpktnXuTeH8OhmvkziiW5YeSUGgECAOAkQRgZyXmXuz93Hj+MnFtRJL9tKdqX0IHOvkluGAAApx/CyEjelQojb/5Wih855qohv0/nTHdnbuVQDQAA2SOMjKRigVQ6W0r0uYHkOM5PHap5JUIRKwAA2SKMjMSyBh2q+dVxV5+fKmJ9iZERAACyRhgZTfpQzauPHnc21vlVqZERwggAAFkjjIwmi9lYF6RGRt5s71FfP9PCAwCQDcLIaPwh6ZwPuvePc4rv9OKQpjEtPAAA40IYOZb5H3N/Pn+/dIw5RNxp4QfmGwEAAGNHGDmW+R9zD9UcfkPa03LsVavSM7EyMgIAQDYII8cSKpIu+LR7/7m7j7kq08IDADA+hJHjWfJF9+eLD0qx0Uc9MmfURLqYFh4AgCwQRo6nepl0xjyp/4j0wgOjrnZuRZF8tqXO3n5FokwLDwDAWBFGjseypCVfcO8f41BNOODTOdOnSOJQDQAA2SCMjMWiz0uWT3prm3Ro56irUcQKAED2CCNjUVwlzfuIe/8YoyMUsQIAkD3CyFi9J1XI+of7pGT/iKvM54J5AABkjTAyVvM+Ik2ZLvUclF57bMRV0tPCv3Gom2nhAQAYI8LIWPkCbu2IJD1314irVAyaFp5DNQAAjA1hJBuLr3J/vvaYdOTwsIcty9KS6jJJUuvud3LYMAAATl2EkWxUnC9VXCA5/dIrD4+4Ss3caZKkZ3YNDysAAGA4wki2Fn7G/fnC/x3x4YvmTpUkPbvrHWZiBQBgDAgj2UqHkTd/J3UfHPbwu88sVdBv6+2euN5s78lx4wAAOPUQRrI17Wxp5nsk40gv/XzYwyG/T4vOLJUkPUvdCAAAx0UYGY+Fn3V/jnKtmnTdyLPUjQAAcFyEkfG44NPuzz1PSZ37hj08uG4EAAAcG2FkPEpnSbMvce+/+OCwh5fOdkdG3mjvUXt3LJctAwDglEMYGa9jnFVTWhjQeZXu1PCMjgAAcGyEkfFa8CnJsqX926XDbwx7uCZzqIa6EQAAjoUwMl5F06WzLnPvj1DIelF68jPOqAEA4JgIIycic1bN/5WOmuAsPTLy4r5OHYknct0yAABOGYSRE3H+xyRfSDr4krSvdchDs8oKVFUSVsIx2rG3w5v2AQBwCiCMnIiCqQOFrNvuHPKQZVmZ0ZFWilgBABgVYeREXbTa/fniA1LP20Mfom4EAIDjIoycqDOXSjOXSMm49Nz/GfJQemRk++53lHS4aB4AACMhjEyE9OjIM5slJ5lZPL+qREUhv7pjCb0SiXrUOAAATm6EkYmw8DNu/UjnHum1/84s9tlWZmr4n+/Y71XrAAA4qRFGJkKgQFryBff+M/825KEvXDxHknTP73er80h/rlsGAMBJjzAyUWq+LMmSXv+19PafMov/bH6FzqssVk88qbt+v8uz5gEAcLIijEyUaWdJ8z7s3n92c2axZVm6+gPnSJI2/3+71BtPjrQ1AAB5izAykdKFrM/dJfUNFKx+7MIZOnNqgQ73xPWfz+71qHEAAJycCCMT6dwPSdPOkfo6pV82ZqaI9/ts/a/3ny1JuuN3b6g/6XjZSgAATiqEkYlk+6RP3S5ZPun5+90RkpS/qKlWeVFQ+zp69V9/4MwaAADSCCMTbXat9KEb3PuP/J3U9pIkKRzw6S/fe5Yk6fYn/iSHSdAAAJBEGJkcl6yRzq2XEr3S/V+S4j2S3NN8i0N+vXawW3f9freMIZAAAEAYmQy2LX36X6XiGVL7TumRv5UklYQDarh0riRp/S9e1LX/8Zw6jsQ9bCgAAN6zzDj+e75x40bdfPPNikQiWrRokW677TYtW7Zs1PXvv/9+3XDDDdq1a5fmzZunH/zgB/roRz865ueLRqMqLS1VZ2enSkpKsm2ud3Y9Kf3045JxpDnvld7/N0rMeb/+92/f0L80v6aEY1RRHNI/fe5CfeC8Cq9bmxPGGMWTjvr6HcUSSSWSRj7bkm1Z8tuWbNv96UvfLEv9jqP+pFE84ag/6cgxRrZlyZIkSwr6bIUDPoX8tizLkiT19Sf1dk9ch7vjivb1yzFGjnGf3xipP+nusz/pKJ50dCSWUHcsoa5YQt19CQV8tkoKAipN3aYEffL7bPl9lgK2rXDAVllhQGWFQZUVBOT3Dc/1XX392tfRq7cO9+pAZ68kqTDoV2HQp8KQX1OCPk0J+TXFTmjq7l9JsS7tn3W5Iokpau+Kqbc/qenFIVWVhFVVGlZ5UUg+2xr2PLFEUp29/erqS8iSFAr4FPbbCgV8si0p6Rg5jpQ0Rj7L0pSQb8T2HovjGHX09qu9OyZLUnE4oOKw+1rSfT7W379jJNtSVtsN3r6v31E84YzrdQDIrbF+f2cdRrZs2aKVK1dq06ZNqq2t1YYNG3T//fdr586dqqgY/oX61FNP6f3vf7+ampr0sY99TPfee69+8IMfaPv27Vq4cOGEvpiT0tN3SP99vXshPUmaVSO97//Vi/4F+uuH3tSf2o9Iks6fUaJ3VRZpXkWR5lUWq7woqIDPlt+2FfS7X9bH+vB2P+SNko6UcBw5R52wY2SUdNxbfzJ1P7WNMe6XVX/SUV8iqd64o77+pOJJR0nHfTyZ+jKLJZKKpYJErN/9Ik8kjRKpwJB0TOb5jZNQYeyQSmP7dUb/fk1PRGQn4zpgpumAmaZ9plyHTYl8VlJBJRRQQj456jBFOqxi9SkkSbLkqFxRzbTaNcM6rF6FtMtUap8pV0L+zGsMWQnNCnTrDHWoKPGOyq1OTVeniq0j6jNB9SisXoXUZ4IKWf0qVJ8KFVOhFVNMAXWZAnWrQD0mrC4VKmoK1aVCdZkCTbH6dLZ1IHMrsXoUSb2G/eYMdfin6x2rTG9bpepVSI4j9RxnTpnp6tAX/L/WVb5fq9xyTwWPmYB+6dTq7kS9tpt5kob+zoOpQOS3Lfl9tnpiCcUS7i87oISMNKRPBrPlqFB9KlBM0wIJlYcSKvAbHU4W6lCiUG8nw+pPGoX9PoUDtkqDRqX+hN7pTSrSk1Sv45M5ajDVtqQpIb8KAj4VBn0KB3wK2wlNi+1XRf9ezUjsU5nzjt5yyrXLma4/JSv1lpmuuAKyLWWCqNFAUEk6RkGfrYKgu8+CoE8yUrSvX9HehOJJRz4lVaXDOidwWGcH31aFr1v7rSrtsqv1liqVtHypNvlVFPKrIOhT0G8rYFvy2bb8qVCXNOn3rFHScdSfcGQnY7ISvTLGUZ9dJMf2y7Is2angG/TbCvnd/aWzoWVJliwlHJMKuu7NsixNCfoyITTot2WMUuHYDcaB1D4DPkt+2079vQ4Ozkn5+joU7G1TKNauuILqDFQqGiyXY/lTvwe3fbblhvhwwP0dFgTc55SUCuPuZ8FIBn8TWJbks9zfjW1b8tkD+/YN+SwavK+BNti25LPd/k6H+PR/IKzUOsYM9L9jjBwn9R8GmYHdpvdnWZnt0m1IdZUcZ6A/rUH9kN4m3dTUf18G/XvQax/yKiQ79b60U7/X9DZ26hee/t0M7q/M8w1a/2jWsDuDHxt4jWOJ6Uc/x7Ctjv7niM85VEFg4gP+pIWR2tpaXXTRRfrRj34kSXIcR9XV1fr617+u6667btj6K1asUE9Pjx5++OHMsosvvliLFy/Wpk2bJvTFnLQ690lP3Sa1/lhK9GUWm2CRDtnT9UJPqXoUVlx+9Ru/EvLJyJIlk3mzuPdNZln6vm0ZDf7LNbLkmNRPWXJkp35a8qX+5Zcjn5Uc9kb0yVFI/QoprpD65beSqf25+5CkAiumAsVVoJjCVlwJ+dRngupVUDEFVaCYSnREpVaPSqwjJ9RtvSaoTk3RVHUrZA2fSj9hbO0z5YoroHKrU1Ot7hN6vonSZwI6rGLFTUDG9sn2+WX7/EpaAcWNT/3Gp4SRFiReVkAJSdJ+M01vmxK9296V2U+7v0rdJqx+xyiRNErKVkyBTH/HFVCZulVhdWi61aHSVH/3mUAmVEnSFKtPU9SnAuvYhwT7jU9dKlBASRWqTz5r+EdDQrYS8ilhfErKVlK2jCzZMvLJkSUz6raDHTEh9SikIyasIwrJyE69W41sGSVkK6Zg5vXaclRsHVGJjqjYOqKp6pbfGvkU+Zjxa5epUoeK1GeC6pN7C6pfU9SnYqtXU9SrsOIKWEn5lVBQCYXUr/AI77NuE1anpihqCtUvv5Lyuf1g/HJkZfogKTvz92nLbVvQSiisuEKKK6y4fHLUL7/i8iuugBzZmb+5sBVXUP2ypEF/6VKZuhS0hgdbx1hq01R1mKLM37jbg0r9rZtMO/oU0hETUq+CqZBv3M8BJeXPvAJ3fVvuey0hX6atkqWg+hVI95XVr0LFFE6F+YASOmLC6laBoipUtylQMrM3pfrJ3V+/cX/aMgpZA583PjmZ502/t5KpV5FMfQZZqffHwGffUOnPPyfzCSlpyH0N+lTVoEfd//Skl6S3T39u+pWU33LkT/29JmXLMXbqNbpbD973aO9+c9R6Qx8b+nsf3vLh7T/6seM9x/FcdOXfa+EFF2a93bGM9ft75P9CjSIej6u1tVVr167NLLNtW/X19WppaRlxm5aWFjU2Ng5Ztnz5cj300EOjPk8sFlMsFsv8Oxo9xa94WzpL+vObpPc1Si0/kv6wReqOyIp3q0Ld+jOf1w08Qcd4zzuWX/GiWUqUzJYpmyNfqECBnoh8XftkRffJOnJY8gUkX0DGF3R31vuOLKdfBVZcBXK/QI1lS8UzZJXMkuLd0uE35U/0ao51cMjzGduvZEG5rKIK2cWVsooqpHCp1N8r9R9xi4n7e93rCQWnSIFC95aMSbEuKdYtxaLura/TnbwuFpV8Ian8XOmMeVL5PKlgmhTdJ6djr5Ide2VF98vubZed6FPY6tdMHR7ol2TqNpLqWqn2q6o872Oq9AWkA9vdqz+/8DOVJyIqT6+XxX9Wwla/wurPjLYczciSCRQq6S+UsXzyxzpkJ/sUsJKapmMHOn8qzGqEL+3Bkv4p6is9W4mys2WKpivYvV+B6G75O96U1X9EhVZMhYpJo7RxLIwdUKL4TMWKzlQ8UKpQ126FO19XKNGr86y3xr3foxVZfSpSn2ZZb0/YPsejx1+mnsAZCjgxFcfb5Fe/ZuiwZliHPW1X2hlWl9dNwAl6pfd/efbcWYWR9vZ2JZNJVVZWDlleWVmpV155ZcRtIpHIiOtHIpFRn6epqUnf+c53smnaqaGoQvrwd91bf6/U+ZbUscf92d/rHspJxqVkv9ycnB5jTI8r2hoYd7QG/p1ezxi3PkWpn8YZWOYk3XlQbJ87D4rtc7cfzPJJ/pDkD7s/bf/AvtLHfQIFA1/k/rDkJNy2J/oGvuTDZVJBmRQuk10wVWHf2N5mmUxjjBsMjrwt9b4jFZ4hq2SmG1rSHEfqjkiH33TbUFQhFVXKCpdlhronTHrwcIRxTltH5YR4j9RzyG17MuG2zSTd36mTHPo7nv4uaeYSSVImj85a6t6W/6N04I+Dfp+p30OiT+rvc4NVMu72dXGlVFQpTZmeakN3Klh1SbKkUJH7+woWS8FCWf6wO5w9uN39vdKRw24A84fcgBYslPwF7vOm25yMua/JSbivx0m4bUu/ryxLChbJV1ShKSONCxvjPk+8y+2r9E0m9X623TY7iYH3VCLm7jdc6t5CJVLhNFlFVQrYtgKD9+847tWz2193n6O/zz2rrb9X8gXdbUNFUrDIfY2+gLvcF3TvBwrc93WgwG1HLOq+B/s63GCa7E+99n73fvr3YpJuf1jW0Nfh87vPk96n5Rv0Hoi7+0r3d/rvTtbQ33vBVKmoUlP8QU0Z/Dp7Dkmde93fWeY9kmqPnfpdWD73sf5UH8R73H5Nt9H2D/rd2QOfKcYMfa8aR/IH3VDuC7r3A1Pc90ig0F0W75Fi6QDf5faJMQNtcxKp91Bqv4M/bwJh99+D/1ac/kGfY6nPIGuEz71hf6uDP/808PxK3T96/fQ+ZSk1LDWwvczA56UvkPpMlNu+9O/86H0PO9gwwnMO/8PItN04zkBbpKGvM7WtSX8/jPwMozzH4Gcb+fF3nfOuY243mbIKI7mydu3aIaMp0WhU1dXVHrZoEgQK3P9hl8/zuiUnH8uSwiXuTWeNvI5tSyUz3Vsu2jNWwSnubercE3vOgqnS2ZeNb9vCadlvEyhwR/BKZ43yeHh8bTmaZUlTznBvk8G23b4/0f5PK5w2vv6cbLbthtDiyuOvi1PKmOpFJr0VuZdVGCkvL5fP51NbW9uQ5W1tbaqqqhpxm6qqqqzWl6RQKKRQKJRN0wAAwCkqq/HsYDCopUuXqrm5ObPMcRw1Nzerrq5uxG3q6uqGrC9Jjz322KjrAwCA/JL1YZrGxkatWrVKNTU1WrZsmTZs2KCenh41NDRIklauXKlZs2apqalJkrRmzRpddtll+uEPf6grrrhC9913n5599lndcccdE/tKAADAKSnrMLJixQodOnRI69atUyQS0eLFi7V169ZMkeqePXtkDyogvOSSS3Tvvffq7//+7/Xtb39b8+bN00MPPTTmOUYAAMDpbVwzsObaKT/PCAAAeWis39/MpQwAADxFGAEAAJ4ijAAAAE8RRgAAgKcIIwAAwFOEEQAA4CnCCAAA8BRhBAAAeOqkvGrv0dLzskWjUY9bAgAAxir9vX28+VVPiTDS1dUlSaqurva4JQAAIFtdXV0qLS0d9fFTYjp4x3G0f/9+FRcXy7KsCdtvNBpVdXW19u7dyzTzE4D+nDj05cSiPycOfTmxTvf+NMaoq6tLM2fOHHLduqOdEiMjtm3rzDPPnLT9l5SUnJZvAq/QnxOHvpxY9OfEoS8n1uncn8caEUmjgBUAAHiKMAIAADyV12EkFApp/fr1CoVCXjfltEB/Thz6cmLRnxOHvpxY9KfrlChgBQAAp6+8HhkBAADeI4wAAABPEUYAAICnCCMAAMBTeR1GNm7cqLlz5yocDqu2tlbbtm3zukknvaamJl100UUqLi5WRUWFPvWpT2nnzp1D1unr69M111yjM844Q0VFRfrsZz+rtrY2j1p86rjppptkWZa+8Y1vZJbRl9nZt2+fvvCFL+iMM85QQUGB3v3ud+vZZ5/NPG6M0bp16zRjxgwVFBSovr5er732moctPjklk0ndcMMNOuuss1RQUKBzzjlH3/ve94ZcX4S+HN3vfvc7ffzjH9fMmTNlWZYeeuihIY+Ppe8OHz6sq666SiUlJSorK9OXv/xldXd35/BV5JjJU/fdd58JBoNm8+bN5sUXXzSrV682ZWVlpq2tzeumndSWL19ufvzjH5sXXnjB7Nixw3z0ox81s2fPNt3d3Zl1vvrVr5rq6mrT3Nxsnn32WXPxxRebSy65xMNWn/y2bdtm5s6day688EKzZs2azHL6cuwOHz5s5syZY770pS+Zp59+2rzxxhvm0UcfNa+//npmnZtuusmUlpaahx56yPzhD38wn/jEJ8xZZ51lent7PWz5yefGG280Z5xxhnn44YfNm2++ae6//35TVFRk/vmf/zmzDn05ukceecRcf/315oEHHjCSzIMPPjjk8bH03eWXX24WLVpkfv/735v/+Z//Meeee6658sorc/xKcidvw8iyZcvMNddck/l3Mpk0M2fONE1NTR626tRz8OBBI8n89re/NcYY09HRYQKBgLn//vsz67z88stGkmlpafGqmSe1rq4uM2/ePPPYY4+Zyy67LBNG6MvsfOtb3zLvfe97R33ccRxTVVVlbr755syyjo4OEwqFzH/8x3/koomnjCuuuML85V/+5ZBln/nMZ8xVV11ljKEvs3F0GBlL37300ktGknnmmWcy6/zqV78ylmWZffv25aztuZSXh2ni8bhaW1tVX1+fWWbbturr69XS0uJhy049nZ2dkqRp06ZJklpbW9Xf3z+kb+fPn6/Zs2fTt6O45pprdMUVVwzpM4m+zNYvfvEL1dTU6C/+4i9UUVGhJUuW6M4778w8/uabbyoSiQzpz9LSUtXW1tKfR7nkkkvU3NysV199VZL0hz/8QU8++aT+/M//XBJ9eSLG0nctLS0qKytTTU1NZp36+nrZtq2nn346523OhVPiQnkTrb29XclkUpWVlUOWV1ZW6pVXXvGoVacex3H0jW98Q5deeqkWLlwoSYpEIgoGgyorKxuybmVlpSKRiAetPLndd9992r59u5555plhj9GX2XnjjTd0++23q7GxUd/+9rf1zDPP6K//+q8VDAa1atWqTJ+N9HdPfw513XXXKRqNav78+fL5fEomk7rxxht11VVXSRJ9eQLG0neRSEQVFRVDHvf7/Zo2bdpp2795GUYwMa655hq98MILevLJJ71uyilp7969WrNmjR577DGFw2Gvm3PKcxxHNTU1+v73vy9JWrJkiV544QVt2rRJq1at8rh1p5b//M//1D333KN7771XF1xwgXbs2KFvfOMbmjlzJn2JSZGXh2nKy8vl8/mGnZXQ1tamqqoqj1p1arn22mv18MMP6/HHH9eZZ56ZWV5VVaV4PK6Ojo4h69O3w7W2turgwYN6z3veI7/fL7/fr9/+9rf6l3/5F/n9flVWVtKXWZgxY4YWLFgwZNn555+vPXv2SFKmz/i7P76//du/1XXXXafPf/7zeve7360vfvGL+uY3v6mmpiZJ9OWJGEvfVVVV6eDBg0MeTyQSOnz48Gnbv3kZRoLBoJYuXarm5ubMMsdx1NzcrLq6Og9bdvIzxujaa6/Vgw8+qN/85jc666yzhjy+dOlSBQKBIX27c+dO7dmzh749yoc+9CE9//zz2rFjR+ZWU1Ojq666KnOfvhy7Sy+9dNhp5q+++qrmzJkjSTrrrLNUVVU1pD+j0aiefvpp+vMoR44ckW0P/Xrw+XxyHEcSfXkixtJ3dXV16ujoUGtra2ad3/zmN3IcR7W1tTlvc054XUHrlfvuu8+EQiHzk5/8xLz00kvmr/7qr0xZWZmJRCJeN+2kdvXVV5vS0lLzxBNPmAMHDmRuR44cyazz1a9+1cyePdv85je/Mc8++6ypq6szdXV1Hrb61DH4bBpj6MtsbNu2zfj9fnPjjTea1157zdxzzz2msLDQ3H333Zl1brrpJlNWVmZ+/vOfmz/+8Y/mk5/8JKejjmDVqlVm1qxZmVN7H3jgAVNeXm7+7u/+LrMOfTm6rq4u89xzz5nnnnvOSDK33nqree6558zu3buNMWPru8svv9wsWbLEPP300+bJJ5808+bN49Te09Vtt91mZs+ebYLBoFm2bJn5/e9/73WTTnqSRrz9+Mc/zqzT29trvva1r5mpU6eawsJC8+lPf9ocOHDAu0afQo4OI/Rldv7rv/7LLFy40IRCITN//nxzxx13DHnccRxzww03mMrKShMKhcyHPvQhs3PnTo9ae/KKRqNmzZo1Zvbs2SYcDpuzzz7bXH/99SYWi2XWoS9H9/jjj4/4Oblq1SpjzNj67u233zZXXnmlKSoqMiUlJaahocF0dXV58GpywzJm0JR6AAAAOZaXNSMAAODkQRgBAACeIowAAABPEUYAAICnCCMAAMBThBEAAOApwggAAPAUYQQAAHiKMAIAADxFGAEAAJ4ijAAAAE8RRgAAgKf+f5EgnXPfLU3+AAAAAElFTkSuQmCC",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "m = NeuralProphet(\n",
- " growth=\"off\",\n",
- " yearly_seasonality=False,\n",
- " weekly_seasonality=False,\n",
- " daily_seasonality=False,\n",
- " n_lags=3 * 24,\n",
- " learning_rate=0.01,\n",
- ")\n",
- "m = m.add_lagged_regressor(\"I\", normalize=\"standardize\")\n",
- "df_train, df_test = m.split_df(df, freq=\"H\", valid_p=0.10)\n",
- "\n",
- "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"plot\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " MAE_val | \n",
- " RMSE_val | \n",
- " Loss_val | \n",
- " RegLoss_val | \n",
- " epoch | \n",
- " MAE | \n",
- " RMSE | \n",
- " Loss | \n",
- " RegLoss | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 108 | \n",
- " 17.935251 | \n",
- " 30.441374 | \n",
- " 0.000596 | \n",
- " 0.0 | \n",
- " 108 | \n",
- " 29.827543 | \n",
- " 52.159111 | \n",
- " 0.001429 | \n",
- " 0.0 | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
- "108 17.935251 30.441374 0.000596 0.0 108 29.827543 52.159111 \n",
- "\n",
- " Loss RegLoss \n",
- "108 0.001429 0.0 "
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metrics.tail(1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000
- },
- "id": "G0E4dLGxcbsO",
- "outputId": "9590ed3b-6e76-4fdd-d4bf-c310c04ac7ec",
- "tags": []
- },
- "outputs": [],
- "source": [
- "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
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- "There is something to consider here, if we consider a neural network with at least one hidden layer: Learning Rate matters when training a Neural Network.\n",
- "\n",
- "For a high enough learning rate (probably > 0.1), the gradient seems to vanish and forces the AR net output to 0.\n",
- "An easy way to void this issue is to set the learning rate at a low enough value, likely around 0.01 to 0.001."
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+ "m.set_plotting_backend(\"plotly-static\")\n",
+ "m.plot(forecast)"
+ ]
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+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
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+ "height": 440
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+ "id": "5v-4bpNUvELW",
+ "outputId": "089e3649-a238-4ec1-9593-32c2844d0ee4"
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+ "source": [
+ "forecast = m.predict(df_test)\n",
+ "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
+ "m.plot(forecast)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 871
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+ "outputId": "acc2e734-f506-4ef7-adfb-ab18076b66e1",
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+ "source": [
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+ ]
+ },
+ {
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+ "execution_count": 15,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
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+ "id": "G0E4dLGxcbsO",
+ "outputId": "9590ed3b-6e76-4fdd-d4bf-c310c04ac7ec",
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- "model_id": "2d0abd808b47405da9baed364f6c6931",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "m.plot_parameters()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 440
},
+ "id": "pmx8KTT0cgyR",
+ "outputId": "16062765-4e93-43c6-c434-857ad4b19008"
+ },
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "3d84baf949b842cdb7119e741679cf06",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
+ }
+ ],
+ "source": [
+ "m.plot(forecast[-48:])"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Sparsifying the AR coefficients\n",
+ "By setting an `ar_reg > 0` we can reduce the number of non-zero AR coefficients."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 148,
+ "referenced_widgets": [
+ "4ac0917121f8498698e087259b787dcf",
+ "94108fe9090f47c7ba2216479e0d3fac",
+ "2d8235496ec642af8192f52d9f2692b1",
+ "c94a8ae41b994c55a96ad44806b0f1c7",
+ "24bf564f55644476911a6cf004a395e7",
+ "87c170d1e00742a29e7f797e98c49cc2",
+ "8a192ccc35e94e9f8be85898ed583e2c",
+ "9467345334da47a8beadc770feef952a",
+ "dc468cd35d2b4f0e8eb287689ac15412",
+ "f35fc9cbd82c4187a4cdc08c3ac26998",
+ "aab682cd3df24821a80331720f7c24e5"
+ ]
},
+ "id": "s7faUgnrvGFN",
+ "outputId": "50da2450-767f-4e3b-f03d-d03226d24ff8"
+ },
+ "outputs": [],
+ "source": [
+ "m = NeuralProphet(\n",
+ " growth=\"off\",\n",
+ " yearly_seasonality=False,\n",
+ " weekly_seasonality=False,\n",
+ " daily_seasonality=False,\n",
+ " n_lags=3 * 24,\n",
+ " ar_reg=1,\n",
+ " learning_rate=0.01,\n",
+ ")\n",
+ "\n",
+ "df_train, df_test = m.split_df(sf_pv_df, freq=\"H\", valid_p=0.10)\n",
+ "\n",
+ "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"bar\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "aeb5baf873774ef891ba2efc3ee5aa13",
- "version_major": 2,
- "version_minor": 0
- },
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " MAE_val | \n",
+ " RMSE_val | \n",
+ " Loss_val | \n",
+ " RegLoss_val | \n",
+ " epoch | \n",
+ " MAE | \n",
+ " RMSE | \n",
+ " Loss | \n",
+ " RegLoss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 108 | \n",
+ " 17.369593 | \n",
+ " 31.082371 | \n",
+ " 0.00119 | \n",
+ " 0.000569 | \n",
+ " 108 | \n",
+ " 30.580618 | \n",
+ " 53.85133 | \n",
+ " 0.002069 | \n",
+ " 0.000569 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
+ "108 17.369593 31.082371 0.00119 0.000569 108 30.580618 53.85133 \n",
+ "\n",
+ " Loss RegLoss \n",
+ "108 0.002069 0.000569 "
]
},
+ "execution_count": 18,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "metrics.tail(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "fba4ba8ca06c4ed49f838bbfe8c9e1d3",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ "id": "G0E4dLGxcbsO",
+ "outputId": "9590ed3b-6e76-4fdd-d4bf-c310c04ac7ec",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
]
},
"metadata": {},
"output_type": "display_data"
- },
+ }
+ ],
+ "source": [
+ "m = m.highlight_nth_step_ahead_of_each_forecast(1)\n",
+ "m.set_plotting_backend(\"plotly-static\")\n",
+ "m.plot_parameters()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1-step ahead forecast with Auto-Regression including Integration\n",
+ "Next, we will add the differences of the series as a lagged covariate.\n",
+ "This basically extends the model from AR to ARI, where the I stands for 'integrated' time series."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1383ccd9696245258ad2f0f9d27fb69d",
- "version_major": 2,
- "version_minor": 0
- },
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " ds | \n",
+ " y | \n",
+ " I | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 8757 | \n",
+ " 2015-12-31 22:00:00 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 8758 | \n",
+ " 2015-12-31 23:00:00 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 8759 | \n",
+ " 2016-01-01 00:00:00 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
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+ " \n",
+ "
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+ "
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+ ],
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ " ds y I\n",
+ "8757 2015-12-31 22:00:00 0 0\n",
+ "8758 2015-12-31 23:00:00 0 0\n",
+ "8759 2016-01-01 00:00:00 0 0"
]
},
+ "execution_count": 20,
"metadata": {},
- "output_type": "display_data"
- },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = sf_pv_df.copy(deep=True)\n",
+ "df[\"I\"] = np.append(0, sf_pv_df[\"y\"].values[1:] - sf_pv_df[\"y\"].values[:-1])\n",
+ "df.tail(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m = NeuralProphet(\n",
+ " growth=\"off\",\n",
+ " yearly_seasonality=False,\n",
+ " weekly_seasonality=False,\n",
+ " daily_seasonality=False,\n",
+ " n_lags=3 * 24,\n",
+ " learning_rate=0.01,\n",
+ ")\n",
+ "m = m.add_lagged_regressor(\"I\", normalize=\"standardize\")\n",
+ "df_train, df_test = m.split_df(df, freq=\"H\", valid_p=0.10)\n",
+ "\n",
+ "metrics = m.fit(df_train, freq=\"H\", validation_df=df_test, progress=\"bar\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "b715bdd85f634d4fb71266fe294b7847",
- "version_major": 2,
- "version_minor": 0
- },
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " MAE_val | \n",
+ " RMSE_val | \n",
+ " Loss_val | \n",
+ " RegLoss_val | \n",
+ " epoch | \n",
+ " MAE | \n",
+ " RMSE | \n",
+ " Loss | \n",
+ " RegLoss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 108 | \n",
+ " 17.883141 | \n",
+ " 30.405554 | \n",
+ " 0.000594 | \n",
+ " 0.0 | \n",
+ " 108 | \n",
+ " 29.873787 | \n",
+ " 52.650181 | \n",
+ " 0.001445 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
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+ ],
"text/plain": [
- "Validation: 0it [00:00, ?it/s]"
+ " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n",
+ "108 17.883141 30.405554 0.000594 0.0 108 29.873787 52.650181 \n",
+ "\n",
+ " Loss RegLoss \n",
+ "108 0.001445 0.0 "
]
},
+ "execution_count": 22,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "metrics.tail(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
},
+ "id": "G0E4dLGxcbsO",
+ "outputId": "9590ed3b-6e76-4fdd-d4bf-c310c04ac7ec",
+ "tags": []
+ },
+ "outputs": [
{
"data": {
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",
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