Summary of Using Dynamic Loss Weighting to Boost Improvements in Forecast Stability, by Daan Caljon et al.
Using dynamic loss weighting to boost improvements in forecast stability
by Daan Caljon, Jeff Vercauteren, Simon De Vos, Wouter Verbeke, Jente Van Belle
First submitted to arxiv on: 26 Sep 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 proposed extension to the N-BEATS model for univariate time series point forecasting incorporates forecast stability as an additional optimization objective, alongside accuracy. The composite loss function includes both forecast error and instability components, with a static hyperparameter controlling the impact of stability. This approach is shown to obtain more stable forecasts without compromising accuracy. To further improve stability while maintaining accuracy, this paper investigates dynamic loss weighting algorithms that adaptively adjust loss weights during training. Existing methods are demonstrated to achieve this objective, providing insights into their effectiveness. Additionally, a novel extension to Random Weighting called Task-Aware Random Weighting is proposed and shown to also achieve improved forecast stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to predict future events in time series data. They combined two important goals: being accurate and being stable. The model uses a combination of how well it predicts the future and how consistent its predictions are. This helps ensure that the forecasts are reliable and don’t change drastically with new information. The paper explores ways to make these predictions even more consistent without sacrificing accuracy. |
Keywords
* Artificial intelligence * Hyperparameter * Loss function * Optimization * Time series
