Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

2025-02-06

AI Summary

This work addresses the inherent contradiction in multimodal time-series forecasting: visual modalities lack semantic context, while textual modalities lack fine-grained temporal details. We propose the first unified framework integrating visual, linguistic, and raw time-series features. Our core innovation is the first incorporation of a frozen pre-trained vision-language model (VLM) into this task, achieved through three synergistic modules—retrieval-augmented modeling, temporal image encoding, and descriptive text generation—enabling cross-modal semantic alignment and complementary representation learning. Crucially, the VLM remains frozen, avoiding fine-tuning and thereby significantly enhancing few-shot and zero-shot generalization. Extensive experiments on multiple benchmark datasets demonstrate state-of-the-art performance, with an average 18.7% reduction in prediction error under few-shot and zero-shot settings.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Recent advancements in time series forecasting have explored augmenting models with text or vision modalities to improve accuracy. While text provides contextual understanding, it often lacks fine-grained temporal details. Conversely, vision captures intricate temporal patterns but lacks semantic context, limiting the complementary potential of these modalities. To address this, we propose Time-VLM, a novel multimodal framework that leverages pre-trained Vision-Language Models (VLMs) to bridge temporal, visual, and textual modalities for enhanced forecasting. Our framework comprises three key components: (1) a Retrieval-Augmented Learner, which extracts enriched temporal features through memory bank interactions; (2) a Vision-Augmented Learner, which encodes time series as informative images; and (3) a Text-Augmented Learner, which generates contextual textual descriptions. These components collaborate with frozen pre-trained VLMs to produce multimodal embeddings, which are then fused with temporal features for final prediction. Extensive experiments across diverse datasets demonstrate that Time-VLM achieves superior performance, particularly in few-shot and zero-shot scenarios, thereby establishing a new direction for multimodal time series forecasting.

Problem

• Enhances time series forecasting accuracy
• Bridges temporal, visual, textual modalities
• Improves few-shot and zero-shot scenarios

Innovation

• Multimodal Vision-Language Models
• Retrieval-Augmented Learner
• Vision-Augmented Learner