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

• URL: http://arxiv.org/abs/2502.04395v1
• Date: Thu, 06 Feb 2025 05:59:45 GMT
• Title: Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
• Authors: Siru Zhong, Weilin Ruan, Ming Jin, Huan Li, Qingsong Wen, Yuxuan Liang,
• Abstract summary: Time-VLM is a novel framework that bridges 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.
• Score: 26.4608782425897
• License:
• 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.

Related papers

• TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents [52.13094810313054]
  TimeCAP is a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data. TimeCAP incorporates two independent LLM agents: one generates a textual summary capturing the context of the time series, while the other uses this enriched summary to make more informed predictions. Experimental results on real-world datasets demonstrate that TimeCAP outperforms state-of-the-art methods for time series event prediction.
  arXiv  Detail & Related papers  (2025-02-17T04:17:27Z)
• Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative [65.84249211767921]
  Texts as Time Series (TaTS) considers the time-series-paired texts to be auxiliary variables of the time series. TaTS can be plugged into any existing numerical-only time series models and enable them to handle time series data with paired texts effectively.
  arXiv  Detail & Related papers  (2025-02-13T03:43:27Z)
• TempoGPT: Enhancing Temporal Reasoning via Quantizing Embedding [13.996105878417204]
  We propose a multi-modal time series data construction approach and a multi-modal time series language model (TLM), TempoGPT. We construct multi-modal data for complex reasoning tasks by analyzing the variable-system relationships within a white-box system. Extensive experiments demonstrate that TempoGPT accurately perceives temporal information, logically infers conclusions, and achieves state-of-the-art in the constructed complex time series reasoning tasks.
  arXiv  Detail & Related papers  (2025-01-13T13:47:05Z)
• Unveiling the Potential of Text in High-Dimensional Time Series Forecasting [12.707274099874384]
  We propose a novel framework that integrates time series models with Large Language Models. Inspired by multimodal models, our method combines time series and textual data in the dual-tower structure. Experiments demonstrate that incorporating text enhances high-dimensional time series forecasting performance.
  arXiv  Detail & Related papers  (2025-01-13T04:10:45Z)
• Text2Freq: Learning Series Patterns from Text via Frequency Domain [8.922661807801227]
  Text2Freq is a cross-modality model that integrates text and time series data via the frequency domain. Our experiments on paired datasets of real-world stock prices and synthetic texts show that Text2Freq achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-01T16:11:02Z)
• Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning [22.28251586213348]
  aLLM4TS is an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning. A distinctive element of our framework is the patch-wise decoding layer, which departs from previous methods reliant on sequence-level decoding.
  arXiv  Detail & Related papers  (2024-02-07T13:51:26Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)
• Zero-Shot Video Moment Retrieval from Frozen Vision-Language Models [58.17315970207874]
  We propose a zero-shot method for adapting generalisable visual-textual priors from arbitrary VLM to facilitate moment-text alignment. Experiments conducted on three VMR benchmark datasets demonstrate the notable performance advantages of our zero-shot algorithm.
  arXiv  Detail & Related papers  (2023-09-01T13:06:50Z)
• mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections [104.14624185375897]
  mPLUG is a new vision-language foundation model for both cross-modal understanding and generation. It achieves state-of-the-art results on a wide range of vision-language downstream tasks, such as image captioning, image-text retrieval, visual grounding and visual question answering.
  arXiv  Detail & Related papers  (2022-05-24T11:52:06Z)
• Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models [65.19308052012858]
  Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research. We present VALUE, a set of meticulously designed probing tasks to decipher the inner workings of multimodal pre-training. Key observations: Pre-trained models exhibit a propensity for attending over text rather than images during inference.
  arXiv  Detail & Related papers  (2020-05-15T01:06:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.