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

Understand

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 \method, a novel multimodal framework that leverages pre-trained Vision-Language Models (VLMs) to bridge temporal, visual, and textual modalities for enhanced forecasting.