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：现实世界应用中的常见预测场景涉及同一领域内一组可能异构的时间序列。由于每个时间序列在长度等属性上存在差异，直接对每个时间序列进行预测具有挑战性。本文提出一个通用框架，利用动态时间规整的相似性度量寻找相似时间序列，以k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结果。文中提出了多种平均化实现方式，并从理论角度论证了平均化对预测的有效性。此外，本文还提出了诊断工具，以便深入理解该过程。