Summary of Multivariate Online Linear Regression For Hierarchical Forecasting, by Massil Hihat et al.
Multivariate Online Linear Regression for Hierarchical Forecasting
by Massil Hihat, Guillaume Garrigos, Adeline Fermanian, Simon Bussy
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deterministic online linear regression model allows responses to be multivariate, introducing complexity that existing methods struggle to handle. To tackle this issue, researchers developed MultiVAW, an extension of the Vovk-Azoury-Warmuth algorithm, which achieves logarithmic regret in time. This breakthrough has practical applications in online hierarchical forecasting, allowing for a relaxation of traditional hypotheses and opening up new avenues for analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a special kind of computer learning that can handle many different responses at once. Right now, most methods only work well with one response, but this new approach can handle multiple responses, which is really useful in many situations. The researchers developed a new method called MultiVAW, which is an improvement over existing methods. This means we can analyze and make predictions about complex data sets more accurately. |
Keywords
* Artificial intelligence * Linear regression
