Summary of Timegpt in Load Forecasting: a Large Time Series Model Perspective, by Wenlong Liao et al.
TimeGPT in Load Forecasting: A Large Time Series Model Perspective
by Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Christian Rehtanz, Shouxiang Wang, Dechang Yang, Zhe Yang
First submitted to arxiv on: 7 Apr 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 This paper explores the potential of large time series models in load forecasting when historical data is scarce. The authors construct a time series generative pre-trained transformer (TimeGPT) and train it on massive and diverse datasets, including finance, transportation, and energy data. The model is then fine-tuned using scarce historical load data to adapt to the specific data distribution. Simulation results show that TimeGPT outperforms benchmarks for short look-ahead times on several real datasets with limited training samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make better predictions about how much energy people will use in the future, when we don’t have a lot of historical data to work with. The idea is to use a special kind of computer model called TimeGPT that has been trained on lots of different kinds of data. Then, it gets fine-tuned using the small amount of historical load data we do have. This helps the model make more accurate predictions for short-term forecasts. |
Keywords
* Artificial intelligence * Time series * Transformer
