Time-Series

Time series Forecasting using ARIMA and SARIMAX

Time series Forecasting using ARIMA and Seasonal ARIMA on Perrin Freres monthly champagne sales

fbprophet

Forecasting using Facebook Prophet on monthly-milk-production-pounds.csv dataset

fbprophet2

Forecasting using Facebook Prophet on airline_passengers.csv dataset

Univariate Time Series Analysis and Forecasting using Stacked LSTM

Univariate Time Series Analysis and Forecasting using Stacked LSTM

DARTS

Documentation

Darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, and combine the predictions of several models and external regressors. Darts supports both univariate and multivariate time series and models. The neural networks can be trained on multiple time series, and some of the models offer probabilistic forecasts. Here, in the notebook,DARTS, I have fitted NBEATS model using darts on two time series dataset simultaneously and forecasted for the next 36 months.

Multivariate Time Series using FBProphet

Multivariate Time Series Analysis and Forecasting using FBProphet on Delhi Climate data set.

Time Series with XgBoost vs Fbprophet

Time series analysis, forecast and comparision using XGBoost Regressor and FBProphet on Real Estate data set.