Summary of Foundation Models For Time Series Analysis: a Tutorial and Survey, by Yuxuan Liang et al.
Foundation Models for Time Series Analysis: A Tutorial and Survey
by Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 A recent breakthrough in Foundation Models (FMs) has revolutionized the field of time series analysis by empowering various downstream tasks. This survey aims to provide a comprehensive overview of FMs in time series analysis, focusing on their underlying mechanisms that enable them to excel in this domain. The authors adopt a methodology-centric classification to categorize pivotal elements, including model architectures, pre-training techniques, adaptation methods, and data modalities. By consolidating the latest advancements in FMs for time series analysis, this survey highlights their theoretical underpinnings, recent developments, and potential avenues for future exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series analysis is important because it helps us understand patterns in data that can be used to make predictions or identify trends. Recently, a type of artificial intelligence called Foundation Models (FMs) has been shown to be very good at this kind of analysis. FMs are pre-trained models that can learn from large amounts of data and then fine-tune themselves for specific tasks like time series forecasting. This survey aims to give an overview of how FMs work in time series analysis, highlighting the different ways they can be used and what makes them effective. |
Keywords
* Artificial intelligence * Classification * Time series
