Summary of Scaling-laws For Large Time-series Models, by Thomas D. P. Edwards et al.
Scaling-laws for Large Time-series Models
by Thomas D. P. Edwards, James Alvey, Justin Alsing, Nam H. Nguyen, Benjamin D. Wandelt
First submitted to arxiv on: 22 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 recent study investigates the scalability of transformer-based models for time series forecasting, similar to the well-studied large language models (LLMs). The researchers train decoder-only transformer models on a diverse dataset of heterogeneous time series data and demonstrate power-law scaling with respect to model size, training compute, and dataset size. This finding is analogous to the scaling laws observed in LLMs, which are widely used for natural language processing tasks. The results suggest that architectural details have a minimal impact over broad ranges, allowing practitioners to design models that can handle complex time series forecasting problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has discovered that powerful transformer-based models can be applied to time series forecasting with great success. Just like large language models (LLMs), these models get better as they grow in size and are trained on more data. The study shows that the architecture of these models doesn’t make a big difference, which is helpful for people who want to use them for tasks like predicting stock prices or weather patterns. This breakthrough could lead to new tools for making accurate predictions about complex events. |
Keywords
» Artificial intelligence » Decoder » Natural language processing » Scaling laws » Time series » Transformer
