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We propose a novel approach to the problem of clustering hierarchicallyaggregated time-series data, which has remained an understudied problem thoughit has several commercial applications. We first group time series at eachaggregated level, while simultaneously leveraging local and global information.The proposed method can cluster hierarchical time series (HTS) with differentlengths and structures. For common two-level hierarchies, we employ a combinedobjective for local and global clustering over spaces of discrete probabilitymeasures, using Wasserstein distance coupled with Soft-DTW divergence. Formulti-level hierarchies, we present a bottom-up procedure that progressivelyleverages lower-level information for higher-level clustering. Our final goalis to improve both the accuracy and speed of forecasts for a larger number ofHTS needed for a real-world application. To attain this goal, each time seriesis first assigned the forecast for its cluster representative, which can beconsidered as a "shrinkage prior" for the set of time series it represents.Then this base forecast can be quickly fine-tuned to adjust to the specifics ofthat time series. We empirically show that our method substantially improvesperformance in terms of both speed and accuracy for large-scale forecastingtasks involving much HTS.

Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering