Summary of Tiny Time Mixers (ttms): Fast Pre-trained Models For Enhanced Zero/few-shot Forecasting Of Multivariate Time Series, by Vijay Ekambaram et al.
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
by Vijay Ekambaram, Arindam Jati, Pankaj Dayama, Sumanta Mukherjee, Nam H. Nguyen, Wesley M. Gifford, Chandra Reddy, Jayant Kalagnanam
First submitted to arxiv on: 8 Jan 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 researchers introduce Tiny Time Mixers (TTM), a compact model that excels in multivariate time series forecasting tasks. The model is trained on public TS datasets and uses innovations like adaptive patching, diverse resolution sampling, and resolution prefix tuning to handle varied dataset resolutions with minimal model capacity. TTM outperforms existing benchmarks in zero/few-shot forecasting by 4-40% while reducing computational requirements significantly. The model’s lightweight nature makes it usable even on CPU-only machines, enhancing usability and fostering wider adoption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new model called Tiny Time Mixers (TTM) that is good at predicting things that happen in the future based on data from the past. It works by using some clever tricks to make itself smaller and more efficient, so it can be used even when computers are not very powerful. |
Keywords
* Artificial intelligence * Few shot * Time series
