{"id":2149,"date":"2019-07-07T13:59:36","date_gmt":"2019-07-07T13:59:36","guid":{"rendered":"https:\/\/machinelearningplus.com\/?p=2149"},"modified":"2022-08-30T12:07:11","modified_gmt":"2022-08-30T12:07:11","slug":"vector-autoregression-examples-python","status":"publish","type":"post","link":"https:\/\/machinelearningplus.com\/time-series\/vector-autoregression-examples-python\/","title":{"rendered":"Vector Autoregression (VAR) – Comprehensive Guide with Examples in Python"},"content":{"rendered":"

Vector Autoregression (VAR) is a forecasting algorithm that can be used when two or more time series influence each other. That is, the relationship between the time series involved is bi-directional. In this post, we will see the concepts, intuition behind VAR models and see a comprehensive and correct method to train and forecast VAR models in python using statsmodels<\/code>.<\/em><\/p>\n

Vector Autoregression (VAR) – Comprehensive Guide with Examples in Python. Photo by Kyran Low.<\/img><\/p>\n

Content<\/h2>\n
    \n
  1. Introduction<\/li>\n
  2. Intuition behind VAR Model Formula<\/li>\n
  3. Building a VAR model in Python<\/li>\n
  4. Import the datasets<\/li>\n
  5. Visualize the Time Series<\/li>\n
  6. Testing Causation using Granger’s Causality Test<\/li>\n
  7. Cointegration Test<\/li>\n
  8. Split the Series into Training and Testing Data<\/li>\n
  9. Check for Stationarity and Make the Time Series Stationary<\/li>\n
  10. How to Select the Order (P) of VAR model<\/li>\n
  11. Train the VAR Model of Selected Order(p)<\/li>\n
  12. Check for Serial Correlation of Residuals (Errors) using Durbin Watson Statistic<\/li>\n
  13. How to Forecast VAR model using statsmodels<\/li>\n
  14. Train the VAR Model of Selected Order(p)<\/li>\n
  15. Invert the transformation to get the real forecast<\/li>\n
  16. Plot of Forecast vs Actuals<\/li>\n
  17. Evaluate the Forecasts<\/li>\n
  18. Conclusion<\/li>\n<\/ol>\n

    1. Introduction<\/h2>\n

    First, what is Vector Autoregression (VAR) and when to use it?<\/p>\n

    Vector Autoregression (VAR) is a multivariate forecasting algorithm that is used when two or more time series<\/a> influence each other. <\/p>\n

    That means, the basic requirements in order to use VAR are:<\/p>\n

      \n
    1. You need at least two time series (variables) <\/li>\n
    2. The time series should influence each other.<\/li>\n<\/ol>\n

      Alright. So why is it called ‘Autoregressive’?<\/p>\n

      It is considered as an Autoregressive model because, each variable (Time Series) is modeled as a function of the past values, that is the predictors are nothing but the lags (time delayed value) of the series.<\/p>\n

      Ok, so how is VAR different from other Autoregressive models like AR, ARMA or ARIMA?<\/p>\n

      The primary difference is those models are uni-directional, where, the predictors influence the Y and not vice-versa. Whereas, Vector Auto Regression (VAR) is bi-directional. That is, the variables influence each other. <\/p>\n

      We will go more in detail in the next section.<\/p>\n

      In this article you will gain a clear understanding of:<\/p>\n