Multiple time series forecasting Python

ARIMA and SARIMA AutoRegressive Integrated Moving Average (ARIMA) models are among the most widely used time series forecasting techniques: In an Autoregressive model, the forecasts correspond to a linear combination of past values of the variable.

Using LSTM, time series forecasting models can predict future values based on previous, sequential data . This provides greater accuracy for demand forecasters which results in better decision making for the business.

A Multivariate time series has more than one time-dependent variable . Each variable depends not only on its past values but also has some dependency on other variables. This dependency is used for forecasting future values.