mlsanityData to 7 Oct 2026

Implicit Reasoning in Deep Time Series Forecasting

arXiv 2409.10840 ↗First posted 17 Sep 2024cs.LGcited by 4

On the map: LLM reasoning → Time series reasoning

Abstract (arXiv)

Recently, time series foundation models have shown promising zero-shot forecasting performance on time series from a wide range of domains. However, it remains unclear whether their success stems from a true understanding of temporal dynamics or simply from memorizing the training data. While implicit reasoning in language models has been studied, similar evaluations for time series models have been largely unexplored. This work takes an initial step toward assessing the reasoning abilities of deep time series forecasting models. We find that certain linear, MLP-based, and patch-based Transformer models generalize effectively in systematically orchestrated out-of-distribution scenarios, suggesting underexplored reasoning capabilities beyond simple pattern memorization.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 22 of its 31 references that are in the map. First posted on arXiv 17 Sep 2024.

Builds on

22 of 31 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  4. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  5. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  6. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  7. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  8. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  9. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  10. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  11. 2023A decoder-only foundation model for time-series forecastingcited by 650
  12. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
10 more
  1. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  2. 2023MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questionscited by 292
  3. 2023Physics of Language Models: Part 3.1, Knowledge Storage and Extractioncited by 264
  4. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 232
  5. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 198
  6. 2024Do Large Language Models Latently Perform Multi-Hop Reasoning?cited by 165
  7. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
  8. 2023Physics of Language Models: Part 3.2, Knowledge Manipulationcited by 142
  9. 2014Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognitioncited by 111
  10. 2024Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalizationcited by 78

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  2. 2024Exploring Representations and Interventions in Time Series Foundation Modelscited by 19
  3. 2025Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluationcited by 15
  4. 2025Investigating Compositional Reasoning in Time Series Foundation Modelscited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2024Towards Time Series Reasoning with LLMs
  2. 2025Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
  3. 2024Language Models Still Struggle to Zero-shot Reason about Time Series
  4. 2026OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning
  5. 2025Conversational Time Series Foundation Models: Towards Explainable and Effective Forecasting
  6. 2025Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs
  7. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
  8. 2026Reasoning-Aware Training for Time Series Forecasting