mlsanityData to 7 Oct 2026

PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting

arXiv 2210.08964 ↗First posted 20 Sep 2022stat.ME cs.AI cs.CLcited by 170

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Abstract (arXiv)

This paper presents a new perspective on time series forecasting. In existing time series forecasting methods, the models take a sequence of numerical values as input and yield numerical values as output. The existing SOTA models are largely based on the Transformer architecture, modified with multiple encoding mechanisms to incorporate the context and semantics around the historical data. Inspired by the successes of pre-trained language foundation models, we pose a question about whether these models can also be adapted to solve time-series forecasting. Thus, we propose a new forecasting paradigm: prompt-based time series forecasting (PromptCast). In this novel task, the numerical input and output are transformed into prompts and the forecasting task is framed in a sentence-to-sentence manner, making it possible to directly apply language models for forecasting purposes. To support and facilitate the research of this task, we also present a large-scale dataset (PISA) that includes three real-world forecasting scenarios. We evaluate different SOTA numerical-based forecasting methods and language generation models. The benchmark results with various forecasting settings demonstrate the proposed PromptCast with language generation models is a promising research direction. Additionally, in comparison to conventional numerical-based forecasting, PromptCast shows a much better generalization ability under the zero-shot setting.

Key facts. Cited by 170 papers in the map (data through 7 Oct 2026). Builds on 27 of its 32 references that are in the map. First posted on arXiv 20 Sep 2022.

Builds on

27 of 32 references.

References · 27

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  8. 2020Longformer: The Long-Document Transformercited by 3,362
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15 more
  1. 2021Grounded Language-Image Pre-trainingcited by 1,147
  2. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
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  5. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
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  7. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 504
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  10. 2021A Dataset for Answering Time-Sensitive Questionscited by 150
  11. 2022LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Taskscited by 146
  12. 2021MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Predictioncited by 48
  13. 2020ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Datacited by 43
  14. 2022TACTiS: Transformer-Attentional Copulas for Time Seriescited by 38
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 15; 2024: 55; 2025: 63; 2026: 37.

170 papers.

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  1. 2024Chronos: Learning the Language of Time Seriescited by 712
  2. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  3. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 170
  4. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 165
  5. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  6. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 134
  7. 2024Large Language Models for Time Series: A Surveycited by 113
  8. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  9. 2024SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameterscited by 103
  10. 2024CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patternscited by 102
  11. 2024UniTS: A Unified Multi-Task Time Series Modelcited by 93
  12. 2024VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasterscited by 87
38 more
  1. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  2. 2024Spatial-Temporal Large Language Model for Traffic Predictioncited by 81
  3. 2024Language Models Still Struggle to Zero-shot Reason about Time Seriescited by 81
  4. 2024AutoTimes: Autoregressive Time Series Forecasters via Large Language Modelscited by 63
  5. 2024Empowering Time Series Analysis with Large Language Models: A Surveycited by 60
  6. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  7. 2024Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilitiescited by 48
  8. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  9. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  10. 2025TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecastercited by 36
  11. 2023GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputationcited by 35
  12. 2024SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognitioncited by 35
  13. 2025Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrativecited by 35
  14. 2024Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Predictioncited by 31
  15. 2024Large language models can be zero-shot anomaly detectors for time series?cited by 30
  16. 2024Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learningcited by 25
  17. 2024Zero-shot forecasting of chaotic systemscited by 25
  18. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  19. 2024Empowering Time Series Analysis with Foundation Models: A Comprehensive Surveycited by 23
  20. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  21. 2024Trajectory Flow Matching with Applications to Clinical Time Series Modelingcited by 21
  22. 2025LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimizationcited by 21
  23. 2025Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillationcited by 20
  24. 2024TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generationcited by 18
  25. 2025BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modelingcited by 18
  26. 2025A Survey of AIOps in the Era of Large Language Modelscited by 18
  27. 2024S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecastingcited by 17
  28. 2024Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Taskscited by 15
  29. 2024Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Modelscited by 15
  30. 2024MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysiscited by 15
  31. 2024A Survey of Generative Techniques for Spatial-Temporal Data Miningcited by 14
  32. 2023Large Language Models for Travel Behavior Predictioncited by 13
  33. 2024Can Language Models Use Forecasting Strategies?cited by 13
  34. 2024Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalizationcited by 12
  35. 2025A Survey on Mathematical Reasoning and Optimization with Large Language Modelscited by 12
  36. 2025CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Datacited by 12
  37. 2025BEDTime: A Unified Benchmark for Automatically Describing Time Seriescited by 11
  38. 2025AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecastingcited by 11

50 most cited of 170 All 170 (JSON)