A common forecasting setting in real world applications considers a set of possibly heterogeneous time series of the same domain. Due to different properties of each time series such as length, obtaining forecasts for each individual time series in a straight-forward way is challenging. This paper proposes a general framework utilizing a similarity measure in Dynamic Time Warping to find similar time series to build neighborhoods in a k-Nearest Neighbor fashion, and improve forecasts of possibly simple models by averaging. Several ways of performing the averaging are suggested, and theoretical arguments underline the usefulness of averaging for forecasting. Additionally, diagnostics tools are proposed allowing a deep understanding of the procedure.

翻译：现实世界应用中的一个常见预测场景，考虑了一组可能具有异质性的同领域时间序列。由于每个时间序列的长度等属性不同，直接对每个单独的时间序列进行预测具有挑战性。本文提出一个通用框架，利用基于动态时间规整的相似性度量，以k-最近邻方式构建邻域，并通过平均化改进可能简单模型的预测。研究提出了几种进行平均化的方法，理论论证强调了平均化对预测的有效性。此外，还提出了诊断工具，以便深入理解该过程。