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：在现实世界的应用场景中，常见的一种预测设置涉及同一领域内一组可能具有异质性的时间序列。由于每个时间序列的属性（例如长度）不同，以直接方式对每个个体时间序列进行预测具有挑战性。本文提出一个通用框架，利用动态时间规整中的相似度度量来寻找相似的时间序列，以k近邻的方式构建邻域，并通过平均来改进可能简单模型的预测。我们提出了多种平均方法，并从理论上论证了平均对预测的有效性。此外，还提出了诊断工具，以便深入理解该过程。