Artificial Intelligence

    

Application of Xgboost to Time Series Forecasting by Taking Advantage of Its Powerful Forecasting Performance

Authors: Tatsuhiko Yamato

Xgboost has the best forecasting performance among non-deep learning methods. However, it works well for interpolation problems and regression, but not for future forecasting of time series data that requires extrapolation. I think it is difficult to avoid this tendency even if we add explanatory variables in the background of the data. Possible explanatory variables include lags of a day or several days from the data, months, days, days of the week, holidays, and so on. In fact, the increase or decrease in data values due to these factors is quite possible and can serve as explanatory variables. However, even if you do this, you will not be able to capture the trend.

Comments: 7 Pages.

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Submission history

[v1] 2021-11-14 14:57:39

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