mlsanityData to 7 Oct 2026

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

arXiv 2310.01728 ↗First posted 3 Oct 2023cs.LG cs.AIcited by 680

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Cited by 66 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for time series forecasting are often specialized, necessitating distinct designs for different tasks and applications. While pre-trained foundation models have made impressive strides in NLP and CV, their development in time series domains has been constrained by data sparsity. Recent studies have revealed that large language models (LLMs) possess robust pattern recognition and reasoning abilities over complex sequences of tokens. However, the challenge remains in effectively aligning the modalities of time series data and natural language to leverage these capabilities. In this work, we present Time-LLM, a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities. To augment the LLM's ability to reason with time series data, we propose Prompt-as-Prefix (PaP), which enriches the input context and directs the transformation of reprogrammed input patches. The transformed time series patches from the LLM are finally projected to obtain the forecasts. Our comprehensive evaluations demonstrate that Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models. Moreover, Time-LLM excels in both few-shot and zero-shot learning scenarios.

Key facts. Cited by 680 papers in the map (data through 7 Oct 2026). Builds on 45 of its 63 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

45 of 63 references.

References · 45

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020Language Models are Few-Shot Learnerscited by 32,199
  5. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  6. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  7. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  8. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  9. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  10. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  11. 2020Reformer: The Efficient Transformercited by 1,658
  12. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
33 more
  1. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  2. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  3. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  4. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  5. 2023A Survey on Multimodal Large Language Modelscited by 898
  6. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  7. 2021Multimodal Few-Shot Learning with Frozen Language Modelscited by 657
  8. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  9. 2022Transformers in Time Series: A Surveycited by 489
  10. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  11. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  12. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
  13. 2023Large Language Models as General Pattern Machinescited by 196
  14. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 158
  15. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
  16. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 146
  17. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  18. 2023Large Language Models are Few-Shot Health Learnerscited by 115
  19. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  20. 2021Voice2Series: Reprogramming Acoustic Models for Time Series Classificationcited by 91
  21. 2021Darts: User-Friendly Modern Machine Learning for Time Seriescited by 90
  22. 2022Model Reprogramming: Resource-Efficient Cross-Domain Machine Learningcited by 64
  23. 2018Transfer learning for time series classificationcited by 60
  24. 2023Enhancing Recommender Systems with Large Language Model Reasoning Graphscited by 52
  25. 2023Leveraging Large Language Models for Pre-trained Recommender Systemscited by 42
  26. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  27. 2022Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Datacited by 27
  28. 2023ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoningcited by 26
  29. 2023SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Eventscited by 21
  30. 2022Reprogramming Pretrained Language Models for Antibody Sequence Infillingcited by 20
  31. 2023pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecastingcited by 16
  32. 2020Reprogramming Language Models for Molecular Representation Learningcited by 13
  33. 2023Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognitioncited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 201; 2025: 262; 2026: 211.

680 papers.

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  1. 2024Chronos: Learning the Language of Time Seriescited by 712
  2. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  3. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 198
  4. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  5. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 170
  6. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  7. 2025Chronos-2: From Univariate to Universal Forecastingcited by 158
  8. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  9. 2024Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Seriescited by 120
  10. 2024Large Language Models for Time Series: A Surveycited by 113
  11. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  12. 2024CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuningcited by 105
38 more
  1. 2024ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilitiescited by 104
  2. 2024SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameterscited by 103
  3. 2024Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecastingcited by 102
  4. 2024Approaching Human-Level Forecasting with Language Modelscited by 102
  5. 2024CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patternscited by 102
  6. 2024UniTS: A Unified Multi-Task Time Series Modelcited by 93
  7. 2024From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflectioncited by 93
  8. 2024Revolutionizing Finance with LLMs: An Overview of Applications and Insightscited by 88
  9. 2024VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasterscited by 87
  10. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 86
  11. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  12. 2024Spatial-Temporal Large Language Model for Traffic Predictioncited by 81
  13. 2024Language Models Still Struggle to Zero-shot Reason about Time Seriescited by 81
  14. 2024TSLANet: Rethinking Transformers for Time Series Representation Learningcited by 71
  15. 2024Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretabilitycited by 69
  16. 2024Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracycited by 66
  17. 2024Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysiscited by 65
  18. 2024AutoTimes: Autoregressive Time Series Forecasters via Large Language Modelscited by 63
  19. 2024Empowering Time Series Analysis with Large Language Models: A Surveycited by 60
  20. 2025Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecastingcited by 59
  21. 2025A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspectivecited by 58
  22. 2024Deep Learning for Multivariate Time Series Imputation: A Surveycited by 57
  23. 2025This Time is Different: An Observability Perspective on Time Series Foundation Modelscited by 57
  24. 2024Determinants of LLM-assisted Decision-Makingcited by 55
  25. 2024Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspectivecited by 51
  26. 2024Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Reviewcited by 50
  27. 2025MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answeringcited by 50
  28. 2024Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilitiescited by 48
  29. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  30. 2024TimeSeriesExam: A time series understanding examcited by 47
  31. 2025DBLoss: Decomposition-based Loss Function for Time Series Forecastingcited by 44
  32. 2024Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoningcited by 41
  33. 2024Can LLMs Understand Time Series Anomalies?cited by 41
  34. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  35. 2024A Comprehensive Survey on Data Augmentationcited by 38
  36. 2024From Similarity to Superiority: Channel Clustering for Time Series Forecastingcited by 37
  37. 2024Towards Time Series Reasoning with LLMscited by 36
  38. 2025TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecastercited by 36

50 most cited of 680 All 680 (JSON)

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  1. 2025Fusing Large Language Models with Temporal Transformers for Time Series Forecasting
  2. 2025When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
  3. 2024Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment
  4. 2025Adapting Large Language Models for Time Series Modeling via a Novel Parameter-efficient Adaptation Method
  5. 2024NeST: Neighborhood-aware semantic alignment and temporal modulation for LLM based time series forecasting
  6. 2026LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications
  7. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
  8. 2026Thoth: Mid-Training Bridges LLMs to Time Series Understanding