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Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis

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arxiv 2406.08627 v4 pith:S33JI4M5 submitted 2024-06-12 cs.LG cs.CL

Haoxin Liu , Shangqing Xu , Zhiyuan Zhao , Lingkai Kong , Harshavardhan Kamarthi , Aditya B. Sasanur , Megha Sharma , Jiaming Cui
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Qingsong Wen Chao Zhang B. Aditya Prakash
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classification cs.LGcs.CL
keywords dataseriesmultimodaltime-mmddatasettimedomainsnumerical
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Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data with multimodal domain-specific knowledge, most existing TSA models rely solely on numerical data, overlooking the significance of information beyond numerical series. This oversight is due to the untapped potential of textual series data and the absence of a comprehensive, high-quality multimodal dataset. To overcome this obstacle, we introduce Time-MMD, the first multi-domain, multimodal time series dataset covering 9 primary data domains. Time-MMD ensures fine-grained modality alignment, eliminates data contamination, and provides high usability. Additionally, we develop MM-TSFlib, the first-cut multimodal time-series forecasting (TSF) library, seamlessly pipelining multimodal TSF evaluations based on Time-MMD for in-depth analyses. Extensive experiments conducted on Time-MMD through MM-TSFlib demonstrate significant performance enhancements by extending unimodal TSF to multimodality, evidenced by over 15% mean squared error reduction in general, and up to 40% in domains with rich textual data. More importantly, our datasets and library revolutionize broader applications, impacts, research topics to advance TSA. The dataset is available at https://github.com/AdityaLab/Time-MMD.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis

    cs.AI 2025-10 conditional novelty 7.0 of 10

    TelecomTS is a new observability dataset from 5G networks that preserves absolute scale and supports multi-modal tasks, showing that current time series and language models struggle with abrupt noisy dynamics.

  2. RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A curated 142-billion-point real-world multivariate time series corpus improves zero-shot forecasting when combined with existing synthetic and univariate pretraining data across four foundation models.

  3. Text Reinforcement for Multimodal Time Series Forecasting

    cs.CL 2025-08 conditional novelty 6.0 of 10

    Reinforcement learning trains an LLM to generate improved text from time series, improving multimodal forecasting on Time-MMD.

  4. TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Reinforcement learning with a composite reward lifts Qwen2.5-VL-3B to 75.29% average accuracy on TIMERBED, above prompt-based GPT-4o and classical time-series baselines.

  5. Diffusion Models for Time Series Forecasting: A Survey

    stat.ML 2025-07 conditional novelty 4.0 of 10

    A survey classifies diffusion-based time series forecasting models into a two-axis taxonomy by conditioning source and integration method.

  6. Human in the Loop Adaptive Optimization for Improved Time Series Forecasting

    cs.LG 2025-05 reject novelty 3.0 of 10

    The core idea is standard forecast recalibration, and the reported experiments show mixed, sometimes negative, results with internal table errors, so the claim of consistent improvement fails.

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