Summary of Moirai-moe: Empowering Time Series Foundation Models with Sparse Mixture Of Experts, by Xu Liu et al.
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
by Xu Liu, Juncheng Liu, Gerald Woo, Taha Aksu, Yuxuan Liang, Roger Zimmermann, Chenghao Liu, Silvio Savarese, Caiming Xiong, Doyen Sahoo
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper introduces Moirai-MoE, a novel time series foundation model that tackles the limitations of existing approaches by delegating pattern modeling to sparse mixture of experts (MoE) within Transformers. By eliminating frequency-level specialization, Moirai-MoE reduces reliance on human-defined heuristics and enables automatic token-level specialization. The authors demonstrate the superiority of Moirai-MoE over existing foundation models in both in-distribution and zero-shot scenarios through extensive experiments on 39 datasets. Additionally, this study provides valuable insights into the inner workings of time series MoE foundation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make predictions about future events by using special types of machine learning models called “foundation models”. These models are good at predicting things that don’t have any labeled data, which means we can use them for tasks like weather forecasting or stock market prediction. The problem with these models is that they need to be trained on different types of data, and this makes it hard to make sure they’re working well together. To fix this, the researchers created a new model called Moirai-MoE that can handle many different types of data at once. They tested it on 39 different datasets and found that it works better than other models in some cases. |
Keywords
» Artificial intelligence » Machine learning » Mixture of experts » Time series » Token » Zero shot
