TimeGPT in Load Forecasting: A Large Time Series Model Perspective

• URL: http://arxiv.org/abs/2404.04885v1
• Date: Sun, 7 Apr 2024 09:05:09 GMT
• Title: TimeGPT in Load Forecasting: A Large Time Series Model Perspective
• Authors: Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Christian Rehtanz, Shouxiang Wang, Dechang Yang, Zhe Yang,
• Abstract summary: Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. This paper aims to discuss the potential of large time series models in load forecasting with scarce historical data.
• Score: 38.92798207166188
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and natural language processing, this paper aims to discuss the potential of large time series models in load forecasting with scarce historical data. Specifically, the large time series model is constructed as a time series generative pre-trained transformer (TimeGPT), which is trained on massive and diverse time series datasets consisting of 100 billion data points (e.g., finance, transportation, banking, web traffic, weather, energy, healthcare, etc.). Then, the scarce historical load data is used to fine-tune the TimeGPT, which helps it to adapt to the data distribution and characteristics associated with load forecasting. Simulation results show that TimeGPT outperforms the benchmarks (e.g., popular machine learning models and statistical models) for load forecasting on several real datasets with scarce training samples, particularly for short look-ahead times. However, it cannot be guaranteed that TimeGPT is always superior to benchmarks for load forecasting with scarce data, since the performance of TimeGPT may be affected by the distribution differences between the load data and the training data. In practical applications, we can divide the historical data into a training set and a validation set, and then use the validation set loss to decide whether TimeGPT is the best choice for a specific dataset.

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