mlsanityData to 14 Oct 2026

LAST SToP For Modeling Asynchronous Time Series

arXiv 2502.01922 ↗First posted 4 Feb 2025cs.LG cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

We present a novel prompt design for Large Language Models (LLMs) tailored to Asynchronous Time Series. Unlike regular time series, which assume values at evenly spaced time points, asynchronous time series consist of timestamped events occurring at irregular intervals, each described in natural language. Our approach effectively utilizes the rich natural language of event descriptions, allowing LLMs to benefit from their broad world knowledge for reasoning across different domains and tasks. This allows us to extend the scope of asynchronous time series analysis beyond forecasting to include tasks like anomaly detection and data imputation. We further introduce Stochastic Soft Prompting, a novel prompt-tuning mechanism that significantly improves model performance, outperforming existing fine-tuning methods such as QLoRA. Through extensive experiments on real world datasets, we demonstrate that our approach achieves state-of-the-art performance across different tasks and datasets.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 53 of its 75 references that are in the map. First posted on arXiv 4 Feb 2025.

Builds on

53 of 75 references.

References · 53

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2024The Llama 3 Herd of Modelscited by 14,715
  5. 2023Segment Anythingcited by 8,234
  6. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  7. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  8. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  9. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  10. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,375
  11. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  12. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
38 more
  1. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  2. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  3. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  4. 2016Deep Networks with Stochastic Depthcited by 979
  5. 2021SoundStream: An End-to-End Neural Audio Codeccited by 771
  6. 2024Chronos: Learning the Language of Time Seriescited by 759
  7. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  8. 2023A decoder-only foundation model for time-series forecastingcited by 692
  9. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
  10. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  11. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 614
  12. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 500
  13. 2022Transformers in Time Series: A Surveycited by 497
  14. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
  15. 2024Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Modelscited by 290
  16. 2022Matryoshka Representation Learningcited by 282
  17. 2022GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Recordscited by 238
  18. 2020Transformer Hawkes Processcited by 235
  19. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 174
  20. 2022PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecastingcited by 172
  21. 2019Set Functions for Time Seriescited by 138
  22. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasterscited by 129
  23. 2024Large Language Models for Time Series: A Surveycited by 114
  24. 2023Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingcited by 113
  25. 2023ForecastPFN: Synthetically-Trained Zero-Shot Forecastingcited by 104
  26. 2021Modeling Irregular Time Series with Continuous Recurrent Unitscited by 103
  27. 2015Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videoscited by 93
  28. 2021Transformer Embeddings of Irregularly Spaced Events and Their Participantscited by 93
  29. 2024VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasterscited by 88
  30. 2023AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?cited by 81
  31. 2015SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularitycited by 76
  32. 2022HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequencescited by 57
  33. 2024LLM Processes: Numerical Predictive Distributions Conditioned on Natural Languagecited by 55
  34. 2023LifelongMemory: Leveraging LLMs for Answering Queries in Long-form Egocentric Videoscited by 51
  35. 2019A Variational Auto-Encoder Model for Stochastic Point Processescited by 30
  36. 2023Add and Thin: Diffusion for Temporal Point Processescited by 23
  37. 2022Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Mediacited by 20
  38. 2022Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systemscited by 16

50 most cited of 53 All 53 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025LiSTEN: Learning Soft Token Embeddings for Neural Audio LLMscited by 3
  2. 2025ADiff4TPP: Asynchronous Diffusion Models for Temporal Point Processescited by 2
  3. 2026MILM: Large Language Models for Multimodal Irregular Time Series with Informative Samplingcited by 2
  4. 2026LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applicationscited by 1
  5. 2026Can LLMs Access their World Knowledge for Event Prediction?cited by 0