Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast
通过平均改善异质时间序列预测:食品需求预测应用
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…