TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
Summary: arXiv:2604.10291v1 Announce Type: new
Abstract: Large Language Models (LLMs) have shown promising performance in time series modeling tasks, but do they truly understand time series data? While multiple benchmarks have been proposed to answer this fundamental question, most are manually curated and focus on narrow domains or specific skill sets. To address this limitation, we propose scalable methods for creating comprehensive time series reasoning benchmarks that combine the flexibility of templates with the creativity of LLM agents.
Introduction
The advent of Large Language Models (LLMs) has revolutionized various fields, including natural language processing and, more recently, time series analysis. However, the question remains: do these models genuinely comprehend the complexities inherent in time series data? Addressing this query is crucial for advancing the capabilities of LLMs in practical applications.
Challenges in Current Benchmarks
Current benchmarks for evaluating LLMs in time series tasks often face several limitations:
- Most benchmarks are manually curated, leading to potential biases.
- Many focus only on narrow domains, such as finance or healthcare, restricting their applicability.
- Specific skill sets are emphasized, neglecting broader reasoning capabilities.
Proposed Solution: TimeSeriesExam
To overcome these challenges, we introduce TimeSeriesExam, a multiple-choice benchmark that leverages synthetic time series data. This benchmark evaluates LLMs across five core reasoning categories:
- Pattern Recognition: Identifying trends and patterns within time series data.
- Noise Understanding: Distinguishing between signal and noise in datasets.
- Similarity Analysis: Comparing different time series to find similarities and differences.
- Anomaly Detection: Identifying outliers that deviate from expected patterns.
- Causality: Understanding causal relationships in time series contexts.
Scaling with TimeSeriesExamAgent
Building upon TimeSeriesExam, we developed TimeSeriesExamAgent to automate and scale the benchmarking process. This tool generates benchmarks from real-world datasets across various domains, including:
- Healthcare
- Finance
- Weather
By employing multi-dimensional quality evaluation, we found that the benchmarks produced by TimeSeriesExamAgent exhibit a diversity level comparable to manually curated benchmarks.
Results and Observations
Despite the advancements brought by TimeSeriesExamAgent, our experiments indicate that LLM performance remains limited in two primary areas:
- Abstract Time Series Reasoning: LLMs struggle with complex abstract reasoning tasks.
- Domain-Specific Applications: Performance varies significantly across different real-world contexts.
Conclusion
The development of TimeSeriesExam and TimeSeriesExamAgent marks a significant step towards more comprehensive and scalable evaluation methods for LLMs in time series analysis. While challenges remain, these benchmarks pave the way for further research and improvement in enabling effective time series understanding in large language models. For more information, visit our GitHub repository at TimeSeriesExamAgent.