Summary of Chronos: Learning the Language Of Time Series, by Abdul Fatir Ansari et al.
Chronos: Learning the Language of Time Series
by Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces Chronos, a framework for training probabilistic time series models using transformer-based language model architectures. Chronos tokenizes time series values and trains the models using cross-entropy loss. The authors pretrain models from the T5 family on a large collection of datasets and a synthetic dataset generated via Gaussian processes. In a comprehensive benchmark, Chronos outperforms other methods on training datasets and shows comparable or superior zero-shot performance on new datasets. The results demonstrate that Chronos can leverage time series data to improve forecasting accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chronos is a new way to make predictions about things that change over time, like temperatures or stock prices. It uses special computer models called transformers to learn patterns in the past and make better predictions for the future. The authors trained these models on lots of different datasets and showed that they can work well even if the model hasn’t seen similar data before. This could be a big help for people who need to predict things like weather or traffic. |
Keywords
* Artificial intelligence * Cross entropy * Language model * T5 * Time series * Transformer * Zero shot
