Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
- URL: http://arxiv.org/abs/2404.16563v2
- Date: Wed, 09 Oct 2024 07:39:29 GMT
- Title: Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
- Authors: Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng, Tucker Balch, Manuela Veloso, Svitlana Vyetrenko,
- Abstract summary: Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting.
We introduce a comprehensive taxonomy of time series features, a critical framework that delineates various characteristics inherent in time series data.
This dataset acts as a solid foundation for assessing the proficiency of LLMs in comprehending time series.
- Score: 13.490168087823992
- License:
- Abstract: Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more. In this paper, we propose a framework for rigorously evaluating the capabilities of LLMs on time series understanding, encompassing both univariate and multivariate forms. We introduce a comprehensive taxonomy of time series features, a critical framework that delineates various characteristics inherent in time series data. Leveraging this taxonomy, we have systematically designed and synthesized a diverse dataset of time series, embodying the different outlined features, each accompanied by textual descriptions. This dataset acts as a solid foundation for assessing the proficiency of LLMs in comprehending time series. Our experiments shed light on the strengths and limitations of state-of-the-art LLMs in time series understanding, revealing which features these models readily comprehend effectively and where they falter. In addition, we uncover the sensitivity of LLMs to factors including the formatting of the data, the position of points queried within a series and the overall time series length.
Related papers
- Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative [65.84249211767921]
Texts as Time Series (TaTS) considers the time-series-paired texts to be auxiliary variables of the time series.
TaTS can be plugged into any existing numerical-only time series models and enable them to handle time series data with paired texts effectively.
arXiv Detail & Related papers (2025-02-13T03:43:27Z) - Harnessing Vision Models for Time Series Analysis: A Survey [72.09716244582684]
This survey discusses the advantages of vision models over LLMs in time series analysis.
It provides a comprehensive and in-depth overview of the existing methods, with dual views of detailed taxonomy.
We address the challenges in the pre- and post-processing steps involved in this framework.
arXiv Detail & Related papers (2025-02-13T00:42:11Z) - Position: Empowering Time Series Reasoning with Multimodal LLMs [49.73647759532127]
We argue that multimodal language models (MLLMs) can enable more powerful and flexible reasoning for time series analysis.
We call on researchers and practitioners to leverage this potential by developing strategies that prioritize trust, interpretability, and robust reasoning in MLLMs.
arXiv Detail & Related papers (2025-02-03T16:10:48Z) - ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning [10.854285913078257]
This paper introduces ChatTS, a novel MLLM designed for time series analysis.
ChatTS treats time series as a modality, similar to how vision MLLMs process images.
Time Series Evol-Instruct generates diverse time series Q&As, enhancing the model's reasoning capabilities.
arXiv Detail & Related papers (2024-12-04T08:06:15Z) - Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification [4.5939667818289385]
HiTime is a hierarchical multi-modal model that seamlessly integrates temporal information into large language models.
Our findings highlight the potential of integrating temporal features into LLMs, paving the way for advanced time series analysis.
arXiv Detail & Related papers (2024-10-24T12:32:19Z) - Deep Time Series Models: A Comprehensive Survey and Benchmark [74.28364194333447]
Time series data is of great significance in real-world scenarios.
Recent years have witnessed remarkable breakthroughs in the time series community.
We release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks.
arXiv Detail & Related papers (2024-07-18T08:31:55Z) - Empowering Time Series Analysis with Large Language Models: A Survey [24.202539098675953]
We provide a systematic overview of methods that leverage large language models for time series analysis.
Specifically, we first state the challenges and motivations of applying language models in the context of time series.
Next, we categorize existing methods into different groups (i.e., direct query, tokenization, prompt design, fine-tune, and model integration) and highlight the key ideas within each group.
arXiv Detail & Related papers (2024-02-05T16:46:35Z) - Position: What Can Large Language Models Tell Us about Time Series Analysis [69.70906014827547]
We argue that current large language models (LLMs) have the potential to revolutionize time series analysis.
Such advancement could unlock a wide range of possibilities, including time series modality switching and question answering.
arXiv Detail & Related papers (2024-02-05T04:17:49Z) - Large Language Models for Time Series: A Survey [34.24258745427964]
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision.
LLMs present a significant potential for analysis of time series data, benefiting domains such as climate, IoT, healthcare, traffic, audio and finance.
arXiv Detail & Related papers (2024-02-02T07:24:35Z) - Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
Time series forecasting holds significant importance in many real-world dynamic systems.
We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting.
Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
arXiv Detail & Related papers (2023-10-03T01:31:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.