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

TL;DR

This paper addresses general time-series analysis by bridging continuous numerical data with visual representations through a vision-language multimodal framework, MLLM4TS. The core idea renders each TS channel as a color-coded line plot, fuses visual embeddings from a pretrained vision encoder with numerical TS embeddings, and employs a temporal-aware patch alignment to harmonize modalities. It demonstrates strong, task-robust performance across classification, anomaly detection, and forecasting, including few-shot and zero-shot settings, while analyzing design choices such as image layout, visual encoders, and language backbones. The work highlights the practical potential of combining vision and language models for versatile TS understanding and suggests paths toward lightweight visual frontends and extended multimodal integration.

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.

Figures (12)

• Figure 1: Illustration of the effect of time series visual inspection.
• Figure 2: Overview of the MLLM4TS framework. (a) Multivariate time series are tokenized into patches and rendered as colour-coded line plots; the resulting embeddings are fused and passed to a pretrained LLM, followed by a task-specific output head. (b.1) Early fusion combines modalities before LLM processing. (b.2) Late fusion merges them after separate LLM encoding.
• Figure 3: Performance comparison of using time series only, plot only, and the multi-modal embeddings.
• Figure 4: Model comparison in classification. “*.” in the Transformers indicates the name of *former. The results are averaged from 10 subsets of UEA. See Table \ref{['tab:clf_eval_table']} for full results.
• Figure 5: Performance comparison under (a) few-shot and (b) zero-shot settings. Results are reported using MSE (lower the better), averaged across four forecasting horizons {96, 192, 336, 720}. Full results are provided in Table \ref{['tab:few_shot_eval']}\ref{['tab:zero_shot_table']}.