Multivariate Online Linear Regression for Hierarchical Forecasting
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Abstract (arXiv)
In this paper, we consider a deterministic online linear regression model where we allow the responses to be multivariate. To address this problem, we introduce MultiVAW, a method that extends the well-known Vovk-Azoury-Warmuth algorithm to the multivariate setting, and show that it also enjoys logarithmic regret in time. We apply our results to the online hierarchical forecasting problem and recover an algorithm from this literature as a special case, allowing us to relax the hypotheses usually made for its analysis.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 22 Feb 2024.
Builds on
7 of 24 references.
References · 7
- 2019Online Learning: A Modern Introduction Using Convex Optimizationcited by 417
- 2013Relative Loss Bounds for On-line Density Estimation with the Exponential Family of Distributionscited by 60
- 2014Optimal learning with Bernstein Online Aggregationcited by 35
- 2020A machine learning approach for forecasting hierarchical time seriescited by 29
- 2018Uniform regret bounds over Rd for the sequential linear regression problem with the square losscited by 14
- 2020Online Hierarchical Forecasting for Power Consumption Datacited by 10
- 2021Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridgecited by 10
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Citing papers per year: 2026: 1.
1 paper.