Unleash the Power of Multivariate Time Series Forecasting with Vector Autoregression (VAR) Models: a theoretical introduction

Author(s): David Andres

Originally published on Towards AI.

Photo by Veronica Reverse on Unsplash

There are times when we need to forecast several variables at the same time. For these occasions, traditional methods such as ARIMA or Exponential Smoothing are not sufficient since they are univariate methods.

Vector AutoRegression (VAR) is a statistical model for multivariate time series analysis and forecasting. It is used to capture the relationship between multiple variables as they change over time. In this article, we will discuss what VAR is and how it works for time series forecasting.

Vector Autoregressive (VAR) models extend the capabilities of univariate Autoregressive (AR) models, enabling them to handle multivariate forecasting… Read the full blog for free on Medium.

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Published via Towards AI