Summary of Proactive Model Adaptation Against Concept Drift For Online Time Series Forecasting, by Lifan Zhao et al.
Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting
by Lifan Zhao, Yanyan Shen
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); 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 In this paper, the authors tackle a crucial challenge in time series forecasting: concept drift. They propose Proceed, a novel proactive model adaptation framework that efficiently translates estimated concept drift into parameter adjustments to adapt the model to new data distributions. The framework is trained on synthetic diverse concept drifts and outperforms state-of-the-art online learning methods on five real-world datasets across various forecast models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in predicting what will happen next based on past events. When we try to predict what might happen, the data changes over time, making it harder for our predictions to be accurate. The authors came up with a way to fix this by adjusting the prediction model as new data comes in. They tested their idea and found that it works better than existing methods. |
Keywords
» Artificial intelligence » Online learning » Time series
