Summary of Loss Shaping Constraints For Long-term Time Series Forecasting, by Ignacio Hounie et al.
Loss Shaping Constraints for Long-Term Time Series Forecasting
by Ignacio Hounie, Javier Porras-Valenzuela, Alejandro Ribeiro
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 paper proposes a novel approach to long-term time series forecasting, addressing the issue of disparate error distributions across forecasting steps. Classical and deep learning-based methods often prioritize average performance, which can lead to large errors at specific time-steps. The authors introduce Constrained Learning, a framework that optimizes average performance while respecting a user-defined upper bound on loss at each time-step. This approach, dubbed “loss shaping constraints,” leverages duality results to ensure the problem has a bounded duality gap. A practical Primal-Dual algorithm is proposed to tackle the non-convex optimization problem. The method demonstrates competitive average performance in benchmark datasets while shaping error distributions across the predicted window. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us forecast future events in a series of numbers. Right now, most methods try to make predictions that are close on average, but this can lead to big mistakes at certain points. The authors came up with a new way to do forecasting that takes into account how bad the mistakes can be at each point. This approach tries to find the best forecast while keeping the mistakes from getting too big. They used some math tricks to make sure their method works well and tested it on popular datasets. |
Keywords
* Artificial intelligence * Deep learning * Optimization * Time series
