Does Multimodality Lead to Better Time Series Forecasting?

• URL: http://arxiv.org/abs/2506.21611v1
• Date: Fri, 20 Jun 2025 23:55:56 GMT
• Title: Does Multimodality Lead to Better Time Series Forecasting?
• Authors: Xiyuan Zhang, Boran Han, Haoyang Fang, Abdul Fatir Ansari, Shuai Zhang, Danielle C. Maddix, Cuixiong Hu, Andrew Gordon Wilson, Michael W. Mahoney, Hao Wang, Yan Liu, Huzefa Rangwala, George Karypis, Bernie Wang,
• Abstract summary: It remains unclear whether and under what conditions such multimodal integration consistently yields gains.<n>We evaluate two popular multimodal forecasting paradigms: aligning-based methods, which align time series and text representations; and prompting-based methods, which directly prompt large language models for forecasting.<n>Our findings highlight that on the modeling side, incorporating text information is most helpful given (1) high-capacity text models, (2) comparatively weaker time series models, and (3) appropriate aligning strategies.
• Score: 84.74978289870155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, there has been growing interest in incorporating textual information into foundation models for time series forecasting. However, it remains unclear whether and under what conditions such multimodal integration consistently yields gains. We systematically investigate these questions across a diverse benchmark of 14 forecasting tasks spanning 7 domains, including health, environment, and economics. We evaluate two popular multimodal forecasting paradigms: aligning-based methods, which align time series and text representations; and prompting-based methods, which directly prompt large language models for forecasting. Although prior works report gains from multimodal input, we find these effects are not universal across datasets and models, and multimodal methods sometimes do not outperform the strongest unimodal baselines. To understand when textual information helps, we disentangle the effects of model architectural properties and data characteristics. Our findings highlight that on the modeling side, incorporating text information is most helpful given (1) high-capacity text models, (2) comparatively weaker time series models, and (3) appropriate aligning strategies. On the data side, performance gains are more likely when (4) sufficient training data is available and (5) the text offers complementary predictive signal beyond what is already captured from the time series alone. Our empirical findings offer practical guidelines for when multimodality can be expected to aid forecasting tasks, and when it does not.

Related papers

• DP-GPT4MTS: Dual-Prompt Large Language Model for Textual-Numerical Time Series Forecasting [2.359557447960552]
  We introduce DP-GPT4MTS (Dual-Prompt GPT2-base for Multimodal Time Series), a novel dual-prompt large language model framework.<n>It combines two complementary prompts: an explicit prompt for clear task instructions and a textual prompt for context-aware embeddings from time-stamped data.<n>Experiments conducted on diverse textural-numerical time series datasets demonstrate that this approach outperforms state-of-the-art algorithms in time series forecasting.
  arXiv  Detail & Related papers  (2025-08-06T09:25:05Z)
• Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives [22.10401153489018]
  Time series forecasting traditionally relies on unimodal numerical inputs.<n>We propose a multimodal contrastive learning framework that transforms raw time series into structured visual and textual perspectives.
  arXiv  Detail & Related papers  (2025-06-30T17:59:14Z)
• MoTime: A Dataset Suite for Multimodal Time Series Forecasting [10.574030048563477]
  MoTime is a suite of multimodal time series forecasting datasets.<n>It pairs temporal signals with external modalities such as text, metadata, and images.
  arXiv  Detail & Related papers  (2025-05-21T03:39:42Z)
• TimesBERT: A BERT-Style Foundation Model for Time Series Understanding [72.64824086839631]
  GPT-style models have been positioned as foundation models for time series forecasting.<n>BERT-style architecture has not been fully unlocked for time series understanding.<n>We design TimesBERT to learn generic representations of time series.<n>Our model is pre-trained on 260 billion time points across diverse domains.
  arXiv  Detail & Related papers  (2025-02-28T17:14:44Z)
• 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.<n>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.<n> 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)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context.<n>We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters.<n>We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• Context Matters: Leveraging Contextual Features for Time Series Forecasting [2.9687381456164004]
  We introduce ContextFormer, a novel plug-and-play method to surgically integrate multimodal contextual information into existing forecasting models.<n> ContextFormer effectively distills forecast-specific information from rich multimodal contexts, including categorical, continuous, time-varying, and even textual information.<n>It outperforms SOTA forecasting models by up to 30% on a range of real-world datasets spanning energy, traffic, environmental, and financial domains.
  arXiv  Detail & Related papers  (2024-10-16T15:36:13Z)
• A Practitioner's Guide to Continual Multimodal Pretraining [83.63894495064855]
  Multimodal foundation models serve numerous applications at the intersection of vision and language.<n>To keep models updated, research into continual pretraining mainly explores scenarios with either infrequent, indiscriminate updates on large-scale new data, or frequent, sample-level updates.<n>We introduce FoMo-in-Flux, a continual multimodal pretraining benchmark with realistic compute constraints and practical deployment requirements.
  arXiv  Detail & Related papers  (2024-08-26T17:59:01Z)

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.