What is multivariate time series prediction?

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.

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.

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.