Summary of Foundts: Comprehensive and Unified Benchmarking Of Foundation Models For Time Series Forecasting, by Zhe Li et al.
FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting
by Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu, Jilin Hu, Chenjuan Guo, Aoying Zhou, Qingsong Wen, Christian S. Jensen, Bin Yang
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes a new benchmark, FoundTS, to evaluate and compare various Time Series Forecasting (TSF) foundation models. These pre-trained models exhibit promising inferencing capabilities on new or unseen data. The benchmark covers different TSF foundation models, including those based on large language models and those pretrained on time series. It also supports different forecasting strategies, such as zero-shot, few-shot, and full-shot, facilitating more thorough evaluations. The authors report on an extensive evaluation of TSF foundation models on various datasets from diverse domains with different statistical characteristics. They identify pros, cons, and limitations of existing foundation models and provide directions for future model design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to compare time series forecasting models that can be used in many areas like finance, weather, and energy management. These models are trained on lots of data and can make good predictions even when they’ve never seen the data before. The new benchmark helps people compare these models and figure out which ones work best in different situations. |
Keywords
» Artificial intelligence » Few shot » Time series » Zero shot
