MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysis

2025-10-08

AI Summary

To address the challenges of modeling complex temporal dependencies and cross-channel interactions in multivariate time series, this paper proposes MLLM4TS—a general-purpose time-series analysis framework based on multimodal large language models (MLLMs). Its core innovation lies in introducing a vision branch that renders multivariate time series as color-coded line plots and designing a time-aware image-patch alignment mechanism to enable fine-grained cross-modal alignment between numerical sequences and language representations. By jointly leveraging visual perception and linguistic understanding, MLLM4TS unifies support for classification, forecasting, and anomaly detection. Extensive experiments across multiple standard benchmarks demonstrate that MLLM4TS significantly outperforms unimodal baselines, achieving superior generalization capability and task adaptability without task-specific architectural modifications.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Effective analysis of time series data presents significant challenges due to the complex temporal dependencies and cross-channel interactions in multivariate data. Inspired by the way human analysts visually inspect time series to uncover hidden patterns, we ask: can incorporating visual representations enhance automated time-series analysis? Recent advances in multimodal large language models have demonstrated impressive generalization and visual understanding capability, yet their application to time series remains constrained by the modality gap between continuous numerical data and discrete natural language. To bridge this gap, we introduce MLLM4TS, a novel framework that leverages multimodal large language models for general time-series analysis by integrating a dedicated vision branch. Each time-series channel is rendered as a horizontally stacked color-coded line plot in one composite image to capture spatial dependencies across channels, and a temporal-aware visual patch alignment strategy then aligns visual patches with their corresponding time segments. MLLM4TS fuses fine-grained temporal details from the numerical data with global contextual information derived from the visual representation, providing a unified foundation for multimodal time-series analysis. Extensive experiments on standard benchmarks demonstrate the effectiveness of MLLM4TS across both predictive tasks (e.g., classification) and generative tasks (e.g., anomaly detection and forecasting). These results underscore the potential of integrating visual modalities with pretrained language models to achieve robust and generalizable time-series analysis.

Problem

• Bridging modality gap between time-series data and language models
• Enhancing automated analysis with visual representations of time-series
• Enabling general time-series analysis via multimodal fusion approach

Innovation

• Converts time-series channels into color-coded composite images
• Aligns visual patches with corresponding time segments
• Fuses numerical data with visual representations for analysis