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TimeSeriesExam: A time series understanding exam (2410.14752v1)

Published 18 Oct 2024 in cs.AI and cs.CL

Abstract: LLMs have recently demonstrated a remarkable ability to model time series data. These capabilities can be partly explained if LLMs understand basic time series concepts. However, our knowledge of what these models understand about time series data remains relatively limited. To address this gap, we introduce TimeSeriesExam, a configurable and scalable multiple-choice question exam designed to assess LLMs across five core time series understanding categories: pattern recognition, noise understanding, similarity analysis, anomaly detection, and causality analysis. TimeSeriesExam comprises of over 700 questions, procedurally generated using 104 carefully curated templates and iteratively refined to balance difficulty and their ability to discriminate good from bad models. We test 7 state-of-the-art LLMs on the TimeSeriesExam and provide the first comprehensive evaluation of their time series understanding abilities. Our results suggest that closed-source models such as GPT-4 and Gemini understand simple time series concepts significantly better than their open-source counterparts, while all models struggle with complex concepts such as causality analysis. We believe that the ability to programatically generate questions is fundamental to assessing and improving LLM's ability to understand and reason about time series data.

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Authors (4)
  1. Yifu Cai (23 papers)
  2. Arjun Choudhry (14 papers)
  3. Mononito Goswami (17 papers)
  4. Artur Dubrawski (67 papers)

Summary

A Structured Evaluation of Time Series Understanding in LLMs: Introducing TimeSeriesExam

The recent exploration of LLMs in time series analysis has drawn significant attention. These models have showcased competencies in tasks such as forecasting, anomaly detection, and classification, prompting inquiry into the depth of their understanding of time series data. The paper "TimeSeriesExam: A Time Series Understanding Exam" addresses this by introducing a novel benchmarking framework, TimeSeriesExam, designed to evaluate LLMs’ understanding of core time series concepts through a series of methodically constructed multiple-choice questions.

TimeSeriesExam is presented as a tool to bridge the gap in our understanding of LLM capabilities concerning time series data. The creators have developed a systematic and configurable exam composed of over 700 questions derived from 104 curated templates. These questions span five key categories: pattern recognition, noise understanding, similarity analysis, anomaly detection, and causality analysis. This taxonomy not only finds relevance in practical applications but also guides a structured approach to assess LLMs on fundamental time series concepts essential for interpreting and predicting temporal data.

The assessment leverages state-of-the-art closed-source models (like GPT-4 and Gemini) and open-source counterparts, providing insights into varied performance levels. Importantly, the results illustrate a distinct disparity between closed and open-source models in handling basic time series tasks, emphasizing that closed-source models are currently more adept at simpler time series tasks. However, all models encounter challenges with more sophisticated tasks such as causality analysis, suggesting a persistent gap in reasoning capabilities tied to intricate time series understanding.

A key methodological advancement of TimeSeriesExam is its procedural question generation. By employing a robust template system, the framework achieves diversity and scale without sacrificing control over the difficulty and discriminative power of questions. Item Response Theory (IRT) is used iteratively to refine question sets, thereby enhancing the exam’s capacity to discriminate between models of differing competencies effectively.

The paper further explores tokenization methodologies for inputting time series data into LLMs, comparing image-based tokenization to textual inputs. Findings indicate that image tokenization generally yields superior results, potentially due to the inherent ability of visual data to convey temporal dynamics more intuitively than flattened text data.

The implications of TimeSeriesExam are significant for both the continued development of LLMs tailored for time series understanding and the broader field of machine learning. Practically, it suggests new pathways for improving time series tasks through model adjustments. Theoretically, it invigorates discourse on multimodal learning where models integrate numerical data with visual representations for enhanced comprehension and reasoning.

Looking forward, the paper discusses potential augmentations to TimeSeriesExam, recommending future benchmarks aimed at evaluating models on abstract reasoning tasks, which would extend beyond traditional statistical patterns and delve into context-driven forecasting and etiological reasoning. This work not only serves as a critical evaluation tool but also pushes forward the potential for LLMs to process and understand temporal information more effectively in real-world applications.

In conclusion, TimeSeriesExam represents a pivotal step in assessing and improving the understanding of time series data by LLMs. While it highlights the existing strength of proprietary models, it also identifies areas for future focus, particularly in complex reasoning tasks and model development strategies. This paper sets the stage for subsequent research, emphasizing the need for continuous refinement of benchmarks and the exploration of richer multimodal integration for future AI advancements.

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