Regularized regression for hierarchical forecasting without unbiasedness conditions

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6 Citations (Scopus)


Forecasting a large set of time series with hierarchical aggregation constraints is a central problem for many organizations. However, it is particularly challenging to forecast these hierarchical structures. In fact, it requires not only good forecast accuracy at each level of the hierarchy, but also the coherency between dierent levels, i.e. the forecasts should satisfy the hierarchical aggregation constraints. Given some incoherent base forecasts, the state-of-the-art methods compute revised forecasts based on forecast combination which ensures that the aggregation constraints are satised. However, these methods assume the base forecasts are unbiased and constrain the revised forecasts to be also unbiased. We propose a new forecasting method which relaxes these unbiasedness conditions, and seeks the revised forecasts with the best tradeo between bias and forecast variance. We also present a regularization method which allows us to deal with high-dimensional hierarchies, and provide its theoretical justication. Finally, we compare the proposed method with the state-of-the-art methods both theoretically and empirically. The results on both simulated and real-world data indicate that our methods provide competitive results compared to the state-of-the-art methods.

Original languageEnglish
Title of host publicationKDD 2019
Subtitle of host publicationProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
EditorsYing Li, Rómer Rosales, Evimaria Terzi, George Karypis
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Electronic)9781450362016
Publication statusPublished - 2019
EventACM International Conference on Knowledge Discovery and Data Mining 2019 - Anchorage, United States of America
Duration: 4 Aug 20198 Aug 2019
Conference number: 25th


ConferenceACM International Conference on Knowledge Discovery and Data Mining 2019
Abbreviated titleKDD 2019
Country/TerritoryUnited States of America
Internet address


  • Hierarchical forecasting
  • Regularization
  • Sparsity
  • Time series

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