Summary of Moment: a Family Of Open Time-series Foundation Models, by Mononito Goswami et al.
MOMENT: A Family of Open Time-series Foundation Models
by Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, Artur Dubrawski
First submitted to arxiv on: 6 Feb 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 family of open-source foundation models, called MOMENT, is introduced for general-purpose time series analysis. The pre-training process is challenging due to the lack of a unified public time series repository and diverse data characteristics. To address this, a large and diverse collection of public time series, called Time Series Pile, is compiled. This allows for multi-dataset training and the design of a benchmark to evaluate time series foundation models on various tasks and datasets with limited supervision. Experimental results demonstrate the effectiveness of pre-trained MOMENT models with minimal data and task-specific fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re introducing a new kind of computer model that can help us understand and work with data about things that happen over time, like weather patterns or stock prices. This model is special because it was trained on lots of different datasets to make it really good at understanding all kinds of time series data. We also made a big collection of public time series data so other people can use this model too. The model works pretty well even when we don’t have much training data or guidance. It’s an exciting development that could help us do things like predict future trends and make better decisions. |
Keywords
* Artificial intelligence * Fine tuning * Time series
