Vector AutoRegression — The Multivariate Time Series Forecasting Method We Must Know
In our previous blog, we’ve learnt the basic of multivariate time series and we used XGBoost regression method to forecast a dependent variable.
In this blog, we’ll see how to use VAR model for multivariate time series analysis.
For, univariate time series, please refer my blog — 10 Time Series Forecasting Methods We Should Know.
Vector AutoRegression (VAR)
Vector autoregression (VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time. — reference
This model assumes that the passed time series dataset is stationary; that means, dataset doesn’t have any trend or seasonality. In case of non-stationary data, we need to transform it to a stationary dataset.
Datasets
We’ll use the same dependent variable: UK Industry and Services — Sales data and independent datasets: UK Consumer Price Index, UK Inflation Rate, UK Real Household Gross Disposable Income Per Capita, UK Population as we used in our previous blog. Refer the Datasets section at-Multivariate Time Series Forecasting using XGBoost.
