Towards Time Series Reasoning with LLMs

Abstract: Multi-modal LLMs (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.

• The paper introduces a multi-step framework that integrates feature extraction, contextualization, and deductive reasoning for effective time-series analysis.
• It employs a lightweight encoder atop Mistral-7B with a two-stage training process, combining curriculum learning and chain-of-thought fine-tuning.
• Results demonstrate superior zero-shot reasoning on temporal tasks versus GPT-4o, underscoring its potential applications in anomaly detection and health monitoring.

Insights into Time-Series Reasoning with LLMs

The paper "Towards Time-Series Reasoning with LLMs," authored by Winnie Chow et al., explores a novel approach to leveraging LLMs for time-series data analysis. While recent advancements in LLMs have demonstrated significant success across various domains such as vision, the field of time-series data has remained relatively less explored. This research fills a critical gap by developing a multi-modal time-series LLM, enhancing the capability of LLMs to reason about time-series data through natural language.

Methodological Advancements

At the heart of this study is a three-step methodological framework designed to address the challenges posed by time-series data: feature extraction, contextualization, and deductive reasoning. The authors propose a lightweight time-series encoder that is integrated on top of an LLM, such as Mistral-7B, allowing the model to extract time-series features directly. This approach averts the common inefficiencies found in methods that convert time-series data into textual tokens.

The model undergoes a rigorous two-stage training process. Initially, it utilizes a curriculum learning strategy to train the encoder from scratch, focusing on simple tasks and gradually increasing complexity. Subsequently, the model is fine-tuned using supervised learning on reasoning tasks augmented with chain-of-thought (CoT) reasoning, promoting a logical flow that exploits encoded time-series features.

Numerical Results and Evaluation

The evaluation of this method includes a series of experiments assessing the model's perception, contextualization, and deductive reasoning capabilities. Notably, the proposed model, equipped with a 7B-parameter LLM, demonstrates superior zero-shot performance on a range of time-series reasoning tasks compared to the larger GPT-4o model. This is evidenced by significant improvements in tasks such as etiological reasoning, where generating natural language captions rather than converting time-series to text resulted in better alignment with task requirements.

The results underscore the effectiveness of encoding time-series features directly into the LLM, with Table 1 highlighting a performance increase from 0.272 to 0.387 for the Mistral-7B model compared to its text-based counterpart. Further evidence is provided in the zero-shot classification tasks derived from the UCR Classification Archive, where the proposed model outperformed GPT-4o across several datasets, indicating its capability to generalize to unseen tasks.

Implications and Future Directions

This research advances the integration of LLMs in processing and reasoning about time-series data, presenting a pivotal step towards more versatile AI systems capable of handling complex multi-modal inputs. The lightweight encoder allows LLMs to natively interpret time-series, thus enhancing both efficiency and accuracy. The implications of this work are significant for applications requiring temporal insights, such as anomaly detection and health monitoring.

Future work can further explore the potential of generating time-series data, which could unlock contextualized forecasting capabilities. Additional research could focus on diverse architectural enhancements or training protocols to refine time-series perception and reasoning abilities.

In conclusion, the proposed methodology represents a commendable achievement in the field of AI, expanding the horizons of LLMs in processing and reasoning over time-series data. This work sets the stage for exploring novel applications and enhancing the AI's ability at the intersection of LLMs and temporal analysis.