mlsanityData to 14 Oct 2026

CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting

arXiv 2604.18305 ↗First posted 20 Apr 2026cs.LGcited by 0

On the map: LLM reasoning → Time series reasoning

Abstract (arXiv)

In this paper we investigate forecasting coevolving time series that feature intricate dependencies and nonstationary dynamics by using an LLM Large Language Models approach We propose a novel modeling approach named ContextAware ARLLM CAARL that provides an interpretable framework to decode the contextual dynamics influencing changes in coevolving series CAARL decomposes time series into autoregressive segments constructs a temporal dependency graph and serializes this graph into a narrative to allow processing by LLM This design yields a chainofthoughtlike reasoning path where intermediate steps capture contextual dynamics and guide forecasts in a transparent manner By linking prediction to explicit reasoning traces CAARL enhances interpretability while maintaining accuracy Experiments on realworld datasets validate its effectiveness positioning CAARL as a competitive and interpretable alternative to stateoftheart forecasting methods

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 28 of its 46 references that are in the map. First posted on arXiv 20 Apr 2026.

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28 of 46 references.

References · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  5. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  6. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  7. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  8. 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 277
  9. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 275
  10. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 246
  11. 2022PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecastingcited by 172
  12. 2019Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecastingcited by 161
16 more
  1. 2019High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processescited by 148
  2. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 144
  3. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 127
  4. 2024Large Language Models for Time Series: A Surveycited by 114
  5. 2019A Gentle Introduction to Deep Learning for Graphscited by 108
  6. 2024ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Datacited by 100
  7. 2023Everything is Connected: Graph Neural Networkscited by 85
  8. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  9. 2019Shape and Time Distortion Loss for Training Deep Time Series Forecasting Modelscited by 70
  10. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  11. 2024Can Graph Learning Improve Planning in LLM-based Agents?cited by 43
  12. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  13. 2023Towards Better Serialization of Tabular Data for Few-shot Classification with Large Language Modelscited by 12
  14. 2020Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networkscited by 8
  15. 2019Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysiscited by 4
  16. 2024LLM-ABBA: Understanding time series via symbolic approximationcited by 4

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  4. 2026CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
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  7. 2026CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting
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