Language Models Still Struggle to Zero-shot Reason about Time Series
(2404.11757)Abstract
Time series are critical for decision-making in fields like finance and healthcare. Their importance has driven a recent influx of works passing time series into language models, leading to non-trivial forecasting on some datasets. But it remains unknown whether non-trivial forecasting implies that language models can reason about time series. To address this gap, we generate a first-of-its-kind evaluation framework for time series reasoning, including formal tasks and a corresponding dataset of multi-scale time series paired with text captions across ten domains. Using these data, we probe whether language models achieve three forms of reasoning: (1) Etiological Reasoning - given an input time series, can the language model identify the scenario that most likely created it? (2) Question Answering - can a language model answer factual questions about time series? (3) Context-Aided Forecasting - does highly relevant textual context improve a language model's time series forecasts? We find that otherwise highly-capable language models demonstrate surprisingly limited time series reasoning: they score marginally above random on etiological and question answering tasks (up to 30 percentage points worse than humans) and show modest success in using context to improve forecasting. These weakness showcase that time series reasoning is an impactful, yet deeply underdeveloped direction for language model research. We also make our datasets and code public at to support further research in this direction at https://github.com/behavioral-data/TSandLanguage
Overview
-
This study evaluates the abilities of Language Models (LMs) to reason with time series data, using a new evaluation framework across tasks like etiological reasoning, question answering, and context-aided forecasting.
-
Despite improvements in LMs like GPT-4, they still show significantly limited abilities in time series reasoning when compared to human performance, with notable deficiencies across all tested reasoning tasks.
-
A novel dataset with 230k questions and 8.7k time series-text pairs was developed to rigorously test and challenge LMs' capacities in real-world analogous scenarios.
-
The study prompts further research to develop more capable LMs in time series reasoning, and it provides resources for continued exploration and potential advances in AI.
Assessing Time Series Reasoning in Language Models: A Comprehensive Study
Introduction
In recent efforts to enhance the applicability of Language Models (LMs) in real-world domains, the abilities of these models to understand and generate time series data have become a vital area of research. This study introduces an innovative evaluation framework to rigorously assess time series reasoning across multiple dimensions, including etiological reasoning, question answering, and context-aided forecasting. Despite high expectations, the study reveals that current LMs, including advanced versions like GPT-4, exhibit limited reasoning capabilities over time series data compared to human performance. This gap highlights significant challenges and opens avenues for future enhancements in this field.
Evaluation Framework and Dataset
The evaluation framework proposed in this paper is designed to test the capacity of LMs to reason about time series data through three distinct reasoning tasks:
- Etiological Reasoning: Testing whether LMs can hypothesize plausible causes for given time series data.
- Question Answering: Assessing the model's ability to answer questions correctly that are contingent upon understanding the time series data.
- Context-Aided Forecasting: Evaluating whether LMs can use contextual text information to enhance forecasting accuracy.
To facilitate this evaluation, the researchers developed a novel dataset comprising 230k multiple-choice questions and 8.7k synthetic time series-text pairs across various scenarios and domains. This extensive dataset underpins a robust testing environment where LMs' reasoning capabilities are systematically challenged against complex, real-world analogous data.
Experimental Findings
Etiological Reasoning
Results indicate that LMs barely perform above random chance in identifying correct scenario descriptions for given time series, with human annotators significantly outperforming the LMs. The best-performing model, GPT-4-Vision, achieved just 34.7% accuracy, starkly lower than the 66.1% human benchmark.
Question Answering
The ability of LMs to answer questions based on time series data was also found largely inadequate. When tested with questions requiring analysis between two different time series, LMs scored nearly at random chance levels, substantially lagging behind the human annotator scores. Notably, even the sophisticated GPT-4 only marginally improved performance with access to the time series data, suggesting a limited understanding of the underlying time series processes.
Context-Aided Forecasting
In forecasting tasks, when LMs were provided with contextual descriptions, their performance showed negligible improvement over forecasts without such context. This was somewhat surprising and demonstrated a significant shortcoming in integrating relevant textual information to predict future time series values accurately.
Implications and Future Directions
The study unmistakably underscores a profound deficiency in current LMs concerning time series reasoning, despite their adeptness at other forms of data processing. This revelation calls for targeted research efforts focusing on developing models or training approaches that enhance the understanding and predictive capabilities of LMs regarding time series data.
The provided open-source dataset and codebase present an excellent resource for future research, enabling ongoing investigation and potentially fostering advancements in this critical aspect of AI development.
Conclusion
Overall, this study serves as a benchmark for understanding the current state of LMs in handling time series data and sets a clear mandate for continued research in this area. Improving LMs' proficiency in time series reasoning not only enhances their applicability across various scientific and commercial fields but also elevates their overall utility in automated decision-making systems, where accuracy and reliability are paramount.
Create an account to read this summary for free: