Summary of Data Augmentation Policy Search For Long-term Forecasting, by Liran Nochumsohn and Omri Azencot
Data Augmentation Policy Search for Long-Term Forecasting
by Liran Nochumsohn, Omri Azencot
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a new approach to automatic data augmentation for time-series problems, particularly in long-term forecasting. The method, called TSAA, is designed to be efficient and easy to implement. It involves a two-step process: initially training a non-augmented model, followed by an iterative split procedure that alternates between identifying a robust augmentation policy and refining the model while discarding suboptimal runs. The approach is evaluated on univariate and multivariate forecasting benchmark problems, demonstrating consistent outperformance of several robust baselines. TSAA’s potential integration into prediction pipelines makes it a valuable tool for practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new method called TSAA that helps with time-series predictions by automatically changing the data. This is useful because time-series data can be tricky to work with, and we need better ways to make accurate predictions. The approach involves two steps: first, it trains a model without any changes, then it makes adjustments and refines the process until it gets good results. It works well on different types of forecasting problems and could be useful for people who do this kind of thing. |
Keywords
» Artificial intelligence » Data augmentation » Time series
