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site_name: Skforecast Docs
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repo_url: https://github.com/skforecast/skforecast
site_url: https://skforecast.org
remote_branch: docs_dev
site_description: Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.
site_author: Joaquin Amat Rodrigo and Javier Escobar Ortiz
use_directory_urls: false
copyright: Copyright © 2021 - 2024 Joaquín Amat Rodrigo, Javier Escobar Ortiz

nav:
  - Home: 
    - Welcome to skforecast: README.md
  
  - Intro to Forecasting: introduction-forecasting/introduction-forecasting.md

  - Skforecast 0.14: user_guides/migration-guide.ipynb
  
  - Quick start:
    - Quick start: quick-start/quick-start-skforecast.ipynb
    - Forecaster Parameters: quick-start/forecaster-parameters.md
    - Forecaster Attributes: quick-start/forecaster-attributes.ipynb
    - How to install: quick-start/how-to-install.md

  - User Guides:
    - Table of contents: user_guides/table-of-contents.md
    - Skforecast 0.14 Migration guide: ./user_guides/migration-guide.html
    - Input data: user_guides/input-data.ipynb
    - Single series Forecasters:
      - Recursive multi-step forecasting: user_guides/autoregresive-forecaster.ipynb
      - Direct multi-step forecasting: user_guides/direct-multi-step-forecasting.ipynb
      - ARIMA and SARIMAX forecasting: user_guides/forecasting-sarimax-arima.ipynb
      - Foreasting baseline: user_guides/forecasting-baseline.ipynb
    - Global Forecasters (multiple series):
      - Independent multi-time series forecasting: user_guides/independent-multi-time-series-forecasting.ipynb
      - Series with different lengths and different exogenous variables: user_guides/multi-series-with-different-length-and-different_exog.ipynb
      - Dependent multivariate series forecasting: user_guides/dependent-multi-series-multivariate-forecasting.ipynb
      - Deep learning Recurrent Neural Networks: user_guides/forecasting-with-deep-learning-rnn-lstm.ipynb
    - Feature Engineering:
      - Exogenous variables: user_guides/exogenous-variables.ipynb
      - Window and custom features: user_guides/window-features-and-custom-features.ipynb
      - Categorical features: user_guides/categorical-features.ipynb
      - Calendars features: user_guides/calendar-features.ipynb
      - Data transformation: user_guides/sklearn-transformers-and-pipeline.ipynb
      - Differentiation: user_guides/time-series-differentiation.ipynb
      - Feature selection: user_guides/feature-selection.ipynb
    - Model Evaluation and Tuning:
      - Metrics: user_guides/metrics.ipynb
      - Backtesting forecaster: user_guides/backtesting.ipynb
      - Hyperparameter tuning and lags selection: user_guides/hyperparameter-tuning-and-lags-selection.ipynb
      - Feature selection: user_guides/feature-selection.ipynb
    - Probabilistic Forecasting: user_guides/probabilistic-forecasting.ipynb
    - Model Explainability: user_guides/explainability.ipynb
    - Model deployment:
      - Save and load forecaster: user_guides/save-load-forecaster.ipynb
      - Forecaster in production: user_guides/forecaster-in-production.ipynb
    - Plotting: user_guides/plotting.ipynb
    - Datasets: user_guides/datasets.ipynb
    - Additional Resources:
      - Extract training and prediction matrices: user_guides/training-and-prediction-matrices.ipynb
      - Weighted time series forecasting: user_guides/weighted-time-series-forecasting.ipynb
      - Stacking multiple models: user_guides/stacking-ensemble-models-forecasting.ipynb
      - Forecasting with XGBoost and LightGBM: user_guides/forecasting-xgboost-lightgbm.ipynb
      - Skforecast in GPU: user_guides/skforecast-in-GPU.ipynb
    - FAQ and forecasting tips:
      - Avoid negative predictions when forecasting: faq/non-negative-predictions.ipynb
      - Forecasting time series with missing values: faq/forecasting-time-series-with-missing-values.ipynb
      - Forecasting with delayed historical data: faq/forecasting-with-delayed-historical-data.ipynb
      - Backtesting vs One-step-ahead: faq/parameters-search-backtesting-vs-one-step-ahead.ipynb
      - Cyclical features in time series: faq/cyclical-features-time-series.ipynb
      - Time series aggregation: faq/time-series-aggregation.ipynb
      - Parallelization in skforecast: faq/parallelization-skforecast.ipynb
      - Profiling skforecast: faq/profiling-skforecast.ipynb

  - Examples and tutorials:
    - English: examples/examples_english.md
    - Spanish: examples/examples_spanish.md

  - API Reference:
    - ForecasterRecursive: api/ForecasterRecursive.md
    - ForecasterDirect: api/ForecasterDirect.md
    - ForecasterRecursiveMultiSeries: api/ForecasterRecursiveMultiSeries.md
    - ForecasterDirectMultiVariate: api/ForecasterDirectMultiVariate.md
    - ForecasterRnn: api/ForecasterRnn.md
    - sarimax: api/Sarimax.md
    - ForecasterSarimax: api/ForecasterSarimax.md
    - ForecasterEquivalentDate: api/ForecasterEquivalentDate.md
    - model_selection: api/model_selection.md
    - feature_selection: api/feature_selection.md
    - preprocessing: api/preprocessing.md
    - metrics: api/metrics.md
    - plot: api/plot.md
    - utils: api/utils.md
    - datasets: api/datasets.md
    - exceptions: api/exceptions.md

  - FAQ and Tips:
    - Table of contents: faq/table-of-contents.md
    - Avoid negative predictions when forecasting: faq/non-negative-predictions.ipynb
    - Forecasting time series with missing values: faq/forecasting-time-series-with-missing-values.ipynb
    - Forecasting with delayed historical data: faq/forecasting-with-delayed-historical-data.ipynb
    - Backtesting vs One-step-ahead: faq/parameters-search-backtesting-vs-one-step-ahead.ipynb
    - Cyclical features in time series: faq/cyclical-features-time-series.ipynb
    - Time series aggregation: faq/time-series-aggregation.ipynb
    - Parallelization in skforecast: faq/parallelization-skforecast.ipynb
    - Profiling skforecast: faq/profiling-skforecast.ipynb

  - Releases: releases/releases.md

  - Authors: authors/authors.md

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      Skforecast is a Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.
    keywords: time series, forecasting, machine learning, python, data science, scikit-learn, lightgbm, xgboost, catboost, keras, probabilistic forecasting, multiple time series forecasting