mlsanityData to 7 Oct 2026

Multi-Modal Time Series Prediction via Mixture of Modulated Experts

arXiv 2601.21547 ↗First posted 29 Jan 2026cs.LG cs.AIcited by 1

On the map: Mixture-of-experts → MoE for time series and graphs

Abstract (arXiv)

Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecasting methods leverage textual information such as news reports to improve prediction, but most rely on token-level fusion that mixes temporal patches with language tokens in a shared embedding space. However, such fusion can be ill-suited when high-quality time-text pairs are scarce and when time series exhibit substantial variation in characteristics, thus complicating cross-modal alignment. In parallel, mixture-of-experts (MoE) architectures have proven effective for both time series modeling and multi-modal learning, yet many existing MoE-based modality integration methods still depend on token-level fusion. To address this, we propose Expert Modulation, a new mechanism for multi-modal time series prediction that conditions both routing and expert computation on textual signals, enabling direct and efficient cross-modal control over expert behavior. Through theoretical analysis and experiments, our proposed method demonstrates strong improvements in multi-modal time series prediction. The current code implementation is available at https://github.com/BruceZhangReve/MoME

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in MoE for time series and graphs, one of 12 directions in Mixture-of-experts. Builds on 55 of its 78 references that are in the map. First posted on arXiv 29 Jan 2026.

Builds on

55 of 78 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts14 Oct
  2. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts24 Sep

Other references · 53

  1. 2023Visual Instruction Tuningcited by 7,792
  2. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  3. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
  4. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  5. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  6. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  7. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  8. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  9. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  10. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
  11. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  12. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
38 more
  1. 2016HyperNetworkscited by 1,003
  2. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  3. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  4. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  5. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  6. 2020GANSpace: Discovering Interpretable GAN Controlscited by 612
  7. 2021VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Expertscited by 424
  8. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
  9. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 232
  10. 2019Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directionscited by 172
  11. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 165
  12. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  13. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasterscited by 127
  14. 2024TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignmentcited by 97
  15. 2024From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflectioncited by 93
  16. 2023Scaling Vision-Language Models with Sparse Mixture of Expertscited by 88
  17. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
  18. 2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusioncited by 67
  19. 2024Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysiscited by 65
  20. 2024MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Expertscited by 61
  21. 2025Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecastingcited by 59
  22. 2025MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answeringcited by 50
  23. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  24. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  25. 2025Advancing Expert Specialization for Better MoEcited by 37
  26. 2025ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Datasetcited by 35
  27. 2024A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualizationcited by 31
  28. 2023Mixture-of-Linear-Experts for Long-term Time Series Forecastingcited by 29
  29. 2023Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Expertscited by 28
  30. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 28
  31. 2024Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shiftcited by 23
  32. 2020Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in Francecited by 22
  33. 2020A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Modelscited by 18
  34. 2025Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Seriescited by 16
  35. 2025TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learningcited by 16
  36. 2024M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecturecited by 12
  37. 2025When Does Multimodality Lead to Better Time Series Forecasting?cited by 9
  38. 2025Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecastingcited by 8

50 most cited of 53 All 53 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026When Does Context Routing Help? A Systematic Study of Multi-Modal Fusion in Time Series Forecastingcited by 0

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  5. 2025UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting
  6. 2024Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
  7. 2025Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines
  8. 2025Multimodal Language Models with Modality-Specific Experts for Financial Forecasting from Interleaved Sequences of Text and Time Series