Summary of Fully Automated Correlated Time Series Forecasting in Minutes, by Xinle Wu et al.
Fully Automated Correlated Time Series Forecasting in Minutes
by Xinle Wu, Xingjian Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Bin Yang, Christian S. Jensen
First submitted to arxiv on: 6 Nov 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 This paper proposes a fully automated and highly efficient correlated time series forecasting framework, addressing challenges in existing automated methods. The framework includes a data-driven iterative strategy to prune a large search space, a zero-shot search strategy to identify the optimal model, and a fast parameter adaptation strategy for training. This approach can achieve state-of-the-art accuracy while being much more efficient than existing methods. The paper demonstrates its effectiveness on seven benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using sensors that send correlated time series data to make predictions about what will happen in the future based on past data. Right now, people design models by hand, but computers can do a better job. To make this process easier and faster, the authors propose a new way to search for the best model and train it using the available data. This approach is much more efficient than current methods and can achieve high accuracy. |
Keywords
» Artificial intelligence » Time series » Zero shot
