mlsanityData to 7 Oct 2026

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

arXiv 2502.04395 ↗First posted 6 Feb 2025cs.CV cs.LGcited by 59

On the map: Multimodal LLMs / vision-language models → Chart understanding and anomaly detection

Abstract (arXiv)

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. 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 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. Code is available at https://github.com/CityMind-Lab/ICML25-TimeVLM.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 26 of its 35 references that are in the map. First posted on arXiv 6 Feb 2025.

Builds on

26 of 35 references.

References · 26

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023Visual Instruction Tuningcited by 7,792
  3. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  4. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,848
  5. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  6. 2020Reformer: The Efficient Transformercited by 1,658
  7. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  8. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  9. 2021ViLT: Vision-and-Language Transformer Without Convolution or Region Supervisioncited by 1,224
  10. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  11. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  12. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
14 more
  1. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  2. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  3. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  4. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  5. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  6. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  7. 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 234
  8. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 158
  9. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 139
  10. 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 113
  11. 2024VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasterscited by 87
  12. 2024Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecastingcited by 26
  13. 2019Forecasting with time series imagingcited by 22
  14. 2020Visual Time Series Forecasting: An Image-driven Approachcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 27; 2026: 32.

59 papers.

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  1. 2025Memory in the Age of AI Agentscited by 285
  2. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  3. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 28
  4. 2025Multi-modal Time Series Analysis: A Tutorial and Surveycited by 24
  5. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  6. 2025How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlookcited by 20
  7. 2025Diffusion Models for Time Series Forecasting: A Surveycited by 11
  8. 2025BEDTime: A Unified Benchmark for Automatically Describing Time Seriescited by 11
  9. 2025TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoningcited by 10
  10. 2025Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecastingcited by 8
  11. 2025From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?cited by 7
  12. 2025Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectivescited by 7
38 more
  1. 2025TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gatheringcited by 7
  2. 2025TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectivenesscited by 6
  3. 2026ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Responsecited by 5
  4. 2025Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learningcited by 4
  5. 2025MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysiscited by 4
  6. 2026Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecastingcited by 4
  7. 2026Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoningcited by 3
  8. 2025Semantic-Enhanced Time-Series Forecasting via Large Language Modelscited by 2
  9. 2025Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecastingcited by 2
  10. 2026Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Modelscited by 2
  11. 2026TiMi: Empower Time Series Transformers with Multimodal Mixture of Expertscited by 2
  12. 2026TSRouter: Dynamic Modality-Model Selection for Time Series Reasoningcited by 2
  13. 2025Non-collective Calibrating Strategy for Time Series Forecastingcited by 1
  14. 2025Chain-of-thought Reviewing and Correction for Time Series Question Answeringcited by 1
  15. 2026Multi-Modal Time Series Prediction via Mixture of Modulated Expertscited by 1
  16. 2026Uncertainty-Aware Multimodal Learning via Conformal Shapley Intervalscited by 1
  17. 2026Visual Reasoning over Time Series via Multi-Agent Systemcited by 1
  18. 2026Empowering Time Series Analysis with Large-Scale Multimodal Pretrainingcited by 1
  19. 2026Overcoming the Modality Gap in Context-Aided Forecastingcited by 1
  20. 2026Discrete Prototypical Memories for Federated Time Series Foundation Modelscited by 1
  21. 2026What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditionscited by 1
  22. 2026VESTA: Visual Exploration with Statistical Tool Agentscited by 1
  23. 2025From Time Series Analysis to Question Answering: A Survey in the LLM Eracited by 0
  24. 2025CLIPTime: Time-Aware Multimodal Representation Learning from Images and Textcited by 0
  25. 2025PA-RNet: Perturbation-Aware Residual Network for Robust Multimodal Time Series Forecastingcited by 0
  26. 2025Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecastingcited by 0
  27. 2025TS-P2CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classificationcited by 0
  28. 2025Temporal-Visual Semantic Alignment: A Unified Architecture for Transferring Spatial Priors from Vision Models to Zero-Shot Temporal Taskscited by 0
  29. 2025FiCoTS: Fine-to-Coarse LLM-Enhanced Hierarchical Cross-Modality Interaction for Time Series Forecastingcited by 0
  30. 2026Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Modelscited by 0
  31. 2026VVTRec: Radio Interferometric Reconstruction through Visual and Textual Modality Enrichmentcited by 0
  32. 2026Enhancing few-shot time series forecasting with LLM-guided diffusioncited by 0
  33. 2026VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detectioncited by 0
  34. 2026Generalizing Dynamics Modeling More Easily from Representation Perspectivecited by 0
  35. 2026Solar-VLM: Multimodal Vision-Language Models for Augmented Solar Power Forecastingcited by 0
  36. 2026Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoningcited by 0
  37. 2026SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecastingcited by 0
  38. 2026STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergycited by 0

50 most cited of 59 All 59 (JSON)

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  1. 2026MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs
  2. 2026TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting
  3. 2024MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models
  4. 2026TimeOmni-VL: Unified Models for Time Series Understanding and Generation
  5. 2024On the Feasibility of Vision-Language Models for Time-Series Classification
  6. 2024Towards Time Series Reasoning with LLMs
  7. 2026Image Synthesis as an Intermediate for Controllable Time Series Generation
  8. 2025Harnessing Vision Models for Time Series Analysis: A Survey