LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions

TL;DR

This paper provides an overview of how Large Language Models are being adapted for cross-modal time series analysis, highlighting strategies, applications, and future challenges in integrating textual and time series data.

Contribution

It introduces a taxonomy of approaches for applying LLMs to cross-modal time series tasks and discusses their applications and open challenges.

Findings

Classifies existing approaches into conversion, alignment, and fusion strategies.

Summarizes applications across various downstream tasks.

Identifies open challenges in the field.

Abstract

Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics, leveraging their massive parameters and the shared sequential nature of textual and time series data. However, a cross-modality gap exists between time series and textual data, as LLMs are pre-trained on textual corpora and are not inherently optimized for time series. In this tutorial, we provide an up-to-date overview of LLM-based cross-modal time series analytics. We introduce a taxonomy that classifies existing approaches into three groups based on cross-modal modeling strategies, e.g., conversion, alignment, and fusion, and then discuss their applications across a range of downstream tasks. In addition, we summarize several open challenges. This tutorial aims to expand the practical application of LLMs in solving real-world problems in cross-modal time series analytics while balancing effectiveness and efficiency. Participants will gain a thorough understanding of current advancements, methodologies, and future research directions in cross-modal time series analytics.