TimeGPT in Load Forecasting: A Large Time Series Model Perspective

arXiv:2404.04885v1 Announce Type: new
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 …

abstract accuracy and natural language processing arxiv cases computer computer vision cs.lg data forecast forecasting language language models language processing large language large language models llms machine machine learning machine learning models natural natural language natural language processing paper performance perspective processing progress series timegpt time series type vision