MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysis
This addresses the problem of complex temporal dependencies in multivariate time-series analysis for researchers and practitioners, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the challenge of analyzing time-series data by incorporating visual representations, introducing MLLM4TS, a framework that uses multimodal large language models with a vision branch to render time-series as images and align visual patches with time segments, achieving effective results in classification, anomaly detection, and forecasting on standard benchmarks.
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.