Source-linked AI summary
Towards Time Series Reasoning with LLMs
Winnie Chow, Lauren Gardiner, Haraldur T. Hallgrímsson, Maxwell A. Xu, Shirley You Ren
TL;DR
Natural-language reasoning over time series remains less developed than multimodal reasoning in domains such as vision. The paper trains a lightweight temporal encoder and then fine-tunes an LLM with chain-of-thought-augmented tasks; the resulting 7B model learns temporal features and surpasses GPT-4o on zero-shot reasoning tasks across domains.
Problem
Time-series MLLMs have shown less broad success in natural-language reasoning, and it remains unclear whether LLMs can zero-shot reason over temporal data.
Method
The model encodes normalized time-series patches directly, then applies supervised fine-tuning with chain-of-thought-augmented reasoning tasks.
Results
The 7B-parameter model encodes features such as frequency and magnitude and surpasses GPT-4o on zero-shot reasoning tasks across varied time-series domains.
Takeaways & Limitations
The approach demonstrates that a smaller multimodal language model can generate human-interpretable time-series reasoning and generalize to unseen tasks.
Takeaways & Limitations
The zero-shot tasks include dataset-specific features and thresholds that models cannot be expected to deduce without having seen the data.
Abstract
from arXiv · showhide
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.
1 Introduction
Time-series reasoning in natural language remains underexplored despite the success of multimodal LLMs in other domains. The paper addresses this gap by combining direct temporal encoding with chain-of-thought training, enabling a smaller model to surpass GPT-4o on zero-shot reasoning tasks.
- Time-series MLLMs have not matched vision-focused models in producing precise, human-interpretable temporal insights through natural language.
- The paper frames time-series reasoning as perception, contextualization, and deductive reasoning.
- Existing representations may create a perception bottleneck, while smaller LLMs may lack capabilities needed for contextualization and deduction.
- The approach trains a lightweight time-series encoder and then uses chain-of-thought-augmented supervised fine-tuning to learn reasoning processes.
- A 7B-parameter model encodes features such as frequency and magnitude, including for out-of-distribution data, and surpasses GPT-4o on zero-shot reasoning tasks across domains.
2 Related work
Prior time-series LLM work emphasizes forecasting or question answering, while natural-language reasoning remains limited. Existing approaches often serialize time series as text or omit text generation, potentially losing temporal information.
- 2.1 Time-series forecasting with LLMs: Forecasting-oriented methods attach time-series modules to pretrained LLM backbones but typically do not retain the language-modeling head or generate text.
- 2.2 Time-series question answering with LLMs: Question-answering studies commonly represent time series as text, a conversion that may lose critical information.
- 2.2 Time-series question answering with LLMs: A pretrained encoder for spirometry data outperformed serializing the time series as text, supporting direct temporal encoding.
- 2.2 Time-series question answering with LLMs: A general time-series reasoning benchmark found that none of its benchmarks significantly exceeded random chance.
3 Methodology
The model converts normalized time-series patches into embeddings aligned with an LLM’s word-embedding space, allowing joint textual and temporal input. Training proceeds from encoder warm-up to end-to-end reasoning fine-tuning with chain-of-thought augmentation.
- 3.1 Architecture: Normalized, non-overlapping time-series patches pass through a self-attention encoder and projection layer matching the LLM word-embedding dimension.
- 3.1 Architecture: Text and time-series embeddings are concatenated for autoregressive generation, using Mistral-7B as the backbone.
- 3.1 Architecture: The architecture supports interleaved modalities, multiple domains, multivariate signals, and variable lengths within context and memory limits.
- 3.2 Training: Stage 1 freezes the LLM while curriculum learning trains the encoder and projection layer from synthetic question answering through real-data captioning.
- 3.2 Training: Stage 2 fine-tunes the encoder, projection layer, and LLM end-to-end with LoRA on downstream tasks, mostly augmented with GPT-generated chain-of-thought text.
4 Experiments
Experiments test whether the encoder improves temporal perception and whether the resulting model can contextualize and reason on unseen classification tasks. The model improves caption-based etiological reasoning and performs above chance on most zero-shot datasets, while GPT-4o remains near chance.
- (1) Perception: Does the encoder help the LLM better perceive time-series data?: The perception evaluation uses GPT-4o to reason from generated captions, testing whether captions preserve temporal features relevant to etiological explanations.
- (1) Perception: Does the encoder help the LLM better perceive time-series data?: Captions from the trained time-series model significantly improve GPT-4o etiological reasoning over captions from text-only Mistral-7B, though they underperform GPT-4o captions.
- (1) Perception: Does the encoder help the LLM better perceive time-series data?: The hidden-state visualization examines whether varying synthetic sine-wave characteristics produce corresponding structure in the model’s latent representations.
- (2, 3) Contextualization and Deductive Reasoning: Evaluating reasoning capabilities through zero-shot classification: The zero-shot evaluation uses selected UCR datasets to test contextualization and deductive reasoning on unseen classification tasks.
- (2, 3) Contextualization and Deductive Reasoning: Evaluating reasoning capabilities through zero-shot classification: The selected tasks remain demanding because dataset-specific thresholds and features cannot generally be inferred without prior exposure.
- (2, 3) Contextualization and Deductive Reasoning: Evaluating reasoning capabilities through zero-shot classification: The model performs significantly above chance on a majority of datasets, whereas GPT-4o is consistently close to chance.
5 Conclusion
The paper combines a lightweight time-series encoder with chain-of-thought fine-tuning to generate human-interpretable reasoning about time series and generalize to unseen tasks.
- The approach combines a flexible time-series encoder with supervised fine-tuning on chain-of-thought data to produce human-interpretable reasoning.The encoder improves perception, while chain-of-thought supervision supports reasoning over time-series features.
- The model demonstrates nontrivial generalization to unseen tasks and surpasses GPT-4o on zero-shot time-series reasoning tasks.
- The work aims to connect time-series analysis with complex decision-making by generating text that follows human logic.
B Case study on the reasoning bottleneck
The case study shows that time-series representation strongly affects etiological reasoning, while performance also depends on the model’s reasoning capabilities and plotting choices.
- Etiological reasoning: The benchmark asks models to infer the generating scenario by hypothesizing, comparing, and matching time-series characteristics across four options.
- Representation effects: Higher-resolution plots improved GPT-4o’s performance, indicating that visual input quality matters for models with vision capabilities.
- Representation effects: Performance almost doubled when plotting the time series compared to tokenization, showing that representation substantially affects etiological reasoning.
- Reasoning bottleneck: GPT-4 gained from proxy captions, but other models did not, indicating that reasoning limitations remain beyond modality misalignment.
- Limitations: Text representations are inefficient, while plotting is sensitive to figure size and requires a 10 x 3 figure for GPT-4o to approach human performance.
- Conclusion: The case study concludes that both the time-series perception bottleneck and model reasoning capabilities must be addressed.
C Datasets
The dataset collection combines real-world and synthetic time series across activity recognition, sleep, forecasting, classification, and reasoning settings.
- Annotated time-series data are sparse and costly because annotation often requires domain expertise, motivating synthetic data generation.
- Dataset splits follow the creators’ specifications whenever available.
- Real datasets cover human activities, sleep stages, forecasting, and time-series classification across multiple domains.PAMAP2 provides activity recordings, Sleep contains EEG stages, M4 contains 100,000 forecasting series, and UCR covers 128 classification datasets.
- Synthetic datasets support etiological reasoning, trend recognition, and combinations of trend, seasonality, outliers, and level shifts with generated captions.
D Training details and tasks
The work defines captioning, question answering, classification, and etiological reasoning tasks, using both multiple-choice and free-form formats across training stages. It also examines what the encoder learns through toy-data visualizations and hidden-state clustering on unseen UCR datasets.
- D Training details and tasks: The task suite covers captioning, question answering, classification, and etiological reasoning over time-series inputs.Captioning describes temporal features, QA answers feature-based questions, classification maps series to natural-language classes, and etiological reasoning hypothesizes the generating scenario.
- D Training details and tasks: Tasks use either multiple-choice or free-form responses, with explicit output constraints for formats such as Yes/No answers.These instructions are intended to reduce learning representations tied to response format rather than task content.
- E What did the encoder learn?: The encoder’s learned representations are examined with toy datasets and t-SNE visualizations of hidden states from unseen UCR classification datasets.The supplied passages identify these visualizations but do not report a specific clustering outcome.
F UCR Classification Archive dataset selection protocol
The UCR evaluation is restricted to binary, sufficiently large, naturally interpretable classification datasets with enough contextual information. After filtering, 11 datasets remain, although class differences can still be subtle.
- F UCR Classification Archive dataset selection protocol: The protocol filters for binary tasks, test sets of at least 100 examples, natural time-series, informative descriptions, and commonsense-solvable class distinctions.It excludes pseudo-time-series, datasets without named labels, and classifications heavily dependent on training examples.
- F UCR Classification Archive dataset selection protocol: The selection procedure reduces the archive to 11 datasets, yet the remaining classification tasks remain challenging because class differences are often subtle.The Chinatown example distinguishes weekends from weekdays through differences in rush-hour peaks.
- F UCR Classification Archive dataset selection protocol: The Chinatown examples illustrate why human-interpretable temporal context matters when separating otherwise similar classes.Weekdays are hypothesized to contain more distinct rush-hour peaks than weekends.
G Examples of model-generated outputs
The examples show the model producing long captions for unseen forecasting data and reasoning traces for UCR classification. The Chinatown prompt explicitly constrains the output to a weekend-versus-weekday choice.
- G Examples of model-generated outputs: The model generates a long caption for unseen M4 test data, paired with proxy ground truth produced by GPT-4o from a plot.This example demonstrates textual description generation after training stage 1.
- G Examples of model-generated outputs: A separate Chinatown example presents the model’s chain-of-thought classification reasoning path.The supplied passage identifies the example but does not report its reasoning content or classification outcome.
- G Examples of model-generated outputs: The Chinatown classification example asks the model to infer whether hourly pedestrian counts come from a normal day or a weekend day.The prompt requires responding with exactly one of the options “weekend” or “weekday.”
H Limitations
The current system generates textual responses from time-series and text but does not generate time-series data itself. The authors identify forecasting, architecture, and task diversity as future directions.
- H Limitations: The model does not currently generate time-series data, leaving contextualized forecasting for future work.Although textual predictions are technically possible, the authors consider that approach ineffective.
- H Limitations: Future work should explore alternative encoder designs and improved ways to incorporate mean and variance.
- H Limitations: The model is trained on a limited task set, and increasing task variety could improve reasoning and generalization abilities.