通过“平均”改进异质时间序列预测，及其在食品需求预测中的应用
Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast
AI总结：
针对同领域异质时间序列直接预测困难的问题，提出结合动态时间规整相似度与k近邻构建邻域、通过平均法提升简单模型预测效果的通用框架，并给出多种平均方式、理论依据及诊断工具。
摘要：
现实应用中常见的一类预测场景是处理同一领域内一组可能存在异质性的时间序列。由于各时间序列在长度等属性上存在差异，直接为每个序列生成预测颇具挑战。本文提出一个通用框架，借助动态时间规整（Dynamic Time Warping）中的相似度度量，以k近邻（k-Nearest Neighbor）方式构建邻域寻找相似时间序列，通过平均法提升甚至简单模型的预测效果。文中提出了多种平均实现方式，并从理论层面论证了平均法用于预测的有效性。此外，还提出了诊断工具，可深入理解该流程的运作机制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
