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