Pith. sign in

REVIEW 5 cited by

Towards Time Series Reasoning with LLMs

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.11376 v2 pith:QSDX6SVB submitted 2024-09-17 cs.LG

Winnie Chow , Lauren Gardiner , Haraldur T. Hallgrímsson , Maxwell A. Xu , Shirley You Ren This is my paper
classification cs.LG
keywords time-seriesreasoningdomainsmodelinformationlanguagelearnsmllms
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance in time-series forecasting, very few works show how an LLM could be used for time-series reasoning in natural language. We propose a novel multi-modal time-series LLM approach that learns generalizable information across various domains with powerful zero-shot performance. First, we train a lightweight time-series encoder on top of an LLM to directly extract time-series information. Then, we fine-tune our model with chain-of-thought augmented time-series tasks to encourage the model to generate reasoning paths. We show that our model learns a latent representation that reflects specific time-series features (e.g. slope, frequency), as well as outperforming GPT-4o on a set of zero-shot reasoning tasks on a variety of domains.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ReasonCast: Towards Explainable Time Series Forecasting with Reasoning

    cs.AI 2026-08 conditional novelty 6.0 of 10

    A fine-tuned LLM that states its reasoning, then its forecast, in one response beats specialized forecasters on five synthetic time series patterns.

  2. Enhancing LLM Reasoning for Time Series Classification by Tailored Thinking and Fused Decision

    cs.AI 2025-06 reject novelty 6.0 of 10

    A three-turn prompting framework, ReasonTSC, boosts LLM time series classification by fusing pattern analysis with plug-in model scores, but its evaluation leaks test-set labels into the prompts and overstates the gains.

  3. MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning

    cs.LG 2026-02 reject novelty 5.0 of 10

    MemCast claims LLM time-series forecasting improves when retrieval from a hierarchical memory of patterns, wisdom, and laws conditions reasoning, but the reported gains depend on a test-label-rewarded confidence update.

  4. Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting

    cs.LG 2025-07 conditional novelty 5.0 of 10

    CGF-LLM combines fuzzy time series and PCMCI causal graphs into text input for fine-tuned GPT-2, reporting improved one-step-ahead forecast NRMSE and a reduction in token count on four datasets.

  5. Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

    cs.LG 2025-05 reject novelty 3.0 of 10

    A review of LLM-based time series analysis that proposes several taxonomies, but is undermined by citation errors and a lack of systematic methodology.

Pith tools