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Many methods for time-series forecasting are known in classical statistics,such as autoregression, moving averages, and exponential smoothing. The DeepARframework is a novel, recent approach for time-series forecasting based on deeplearning. DeepAR has shown very promising results already. However, time seriesoften have change points, which can degrade the DeepAR's prediction performancesubstantially. This paper extends the DeepAR framework by detecting andincluding those change points. We show that our method performs as well asstandard DeepAR when there are no change points and considerably better whenthere are change points. More generally, we show that the batch size providesan effective and surprisingly simple way to deal with change points in DeepAR,Transformers, and other modern forecasting models.

The DeepCAR Method: Forecasting Time-Series Data That Have Change Points