Summary of Scaling Law For Time Series Forecasting, by Jingzhe Shi et al.
Scaling Law for Time Series Forecasting
by Jingzhe Shi, Qinwei Ma, Huan Ma, Lei Li
First submitted to arxiv on: 24 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 A novel scaling law for time series forecasting is proposed, which addresses the seemingly abnormal behaviors observed in previous studies. The theory takes into account dataset size, model complexity, and time series data granularity, particularly focusing on the look-back horizon. Empirical evaluation of various models using diverse datasets verifies the validity of the scaling law on dataset size and model complexity, as well as validates the theoretical framework regarding the influence of look-back horizon. This research aims to inspire new models targeting limited-size datasets and large foundational datasets for time series forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers studied how deep learning methods perform in predicting future events based on past data. They found that having more training data helps, but using a more complex model doesn’t always make it better. The study also looked at how far back you can look to get the best results. They developed a theory to explain these findings and tested different models on various datasets. Their results showed that the theory works and can help create new models for predicting future events with limited or large amounts of data. |
Keywords
» Artificial intelligence » Deep learning » Time series
