Efficient Forecasting of Large-Scale Hierarchical Time Series via Multilevel Clustering

Abstract

We propose a novel approach to cluster hierarchical time series (HTS) for efficient forecasting and data analysis. Inspired by a practically important but unstudied problem, we found that leveraging local information when clustering HTS leads to a better performance. The clustering procedure we proposed can cope with massive HTS with arbitrary lengths and structures. In addition to providing better insights, this method can also speed up the forecasting process for a large number of HTS. Each time series is first assigned the forecast from its cluster representative, which can be considered as “prior shrinkage” for the set of time series it represents. Then, the base forecast can be efficiently adjusted to accommodate the specific attributes of the time series. We empirically show that our method substantially improves performance for large-scale clustering and forecasting tasks involving HTS.

Keywords

• hierarchical time series
• clustering
• Wasserstein distance