mlsanityData to 7 Oct 2026

Unveiling the Potential of Text in High-Dimensional Time Series Forecasting

arXiv 2501.07048 ↗First posted 13 Jan 2025cs.AIcited by 4

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Abstract (arXiv)

Time series forecasting has traditionally focused on univariate and multivariate numerical data, often overlooking the benefits of incorporating multimodal information, particularly textual data. In this paper, we propose a novel framework that integrates time series models with Large Language Models to improve high-dimensional time series forecasting. Inspired by multimodal models, our method combines time series and textual data in the dual-tower structure. This fusion of information creates a comprehensive representation, which is then processed through a linear layer to generate the final forecast. Extensive experiments demonstrate that incorporating text enhances high-dimensional time series forecasting performance. This work paves the way for further research in multimodal time series forecasting.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 13 Jan 2025.

Builds on

21 of 33 references.

References · 21

  1. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  2. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  3. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  4. 2023Generative Multimodal Models are In-Context Learnerscited by 446
  5. 2023Generating Images with Multimodal Language Modelscited by 259
  6. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 170
  7. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 165
  8. 2024Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecastingcited by 102
  9. 2023Conformal PID Control for Time Series Predictioncited by 85
  10. 2024TSLANet: Rethinking Transformers for Time Series Representation Learningcited by 71
  11. 2024Fredformer: Frequency Debiased Transformer for Time Series Forecastingcited by 43
  12. 2023Conformal Prediction for Time Series with Modern Hopfield Networkscited by 40
9 more
  1. 2024ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysiscited by 26
  2. 2023MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detectioncited by 20
  3. 2024Generative Pretrained Hierarchical Transformer for Time Series Forecastingcited by 15
  4. 2018Multi-Source Social Feedback of Online News Feedscited by 14
  5. 2023Dynamic Hypergraph Structure Learning for Traffic Flow Forecastingcited by 14
  6. 2023Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Contextcited by 13
  7. 2024MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMscited by 6
  8. 2024Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learningcited by 4
  9. 2022FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time seriescited by 3

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrativecited by 35
  2. 2025Multimodal Conditioned Diffusive Time Series Forecastingcited by 8
  3. 2025Text Reinforcement for Multimodal Time Series Forecastingcited by 6
  4. 2025MoTime: A Dataset Suite for Multimodal Time Series Forecastingcited by 3