A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Mode

arXiv:2405.02358v1 Announce Type: new
Abstract: Time series data are ubiquitous across various domains, making time series analysis critically important. Traditional time series models are task-specific, featuring singular functionality and limited generalization capacity. Recently, large language foundation models have unveiled their remarkable capabilities for cross-task transferability, zero-shot/few-shot learning, and decision-making explainability. This success has sparked interest in the exploration of foundation models to solve multiple time series challenges simultaneously. There are two main research lines, namely \textbf{pre-training foundation models from scratch …

arxiv cs.ai cs.lg foundation language large language representation series survey time series time series foundation models type