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.

翻译：在经典统计学中，已存在多种时间序列预测方法，如自回归、移动平均和指数平滑。DeepAR框架是一种基于深度学习的新颖时间序列预测方法，已展现出十分有前景的结果。然而，时间序列常存在变化点，这可能会显著降低DeepAR的预测性能。本文通过检测并纳入这些变化点来扩展DeepAR框架。我们证明，当不存在变化点时，该方法与标准DeepAR性能相当；而当存在变化点时，其性能显著更优。更广泛地，我们表明批次大小提供了一种有效且出乎意料简单的方式来处理DeepAR、Transformer及其他现代预测模型中的变化点。