Are Large Language Models Useful for Time Series Data Analysis?

• URL: http://arxiv.org/abs/2412.12219v1
• Date: Mon, 16 Dec 2024 02:47:44 GMT
• Title: Are Large Language Models Useful for Time Series Data Analysis?
• Authors: Francis Tang, Ying Ding,
• Abstract summary: Time series data plays a critical role across diverse domains such as healthcare, energy, and finance.<n>This study investigates whether large language models (LLMs) are effective for time series data analysis.
• Score: 3.44393516559102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series data plays a critical role across diverse domains such as healthcare, energy, and finance, where tasks like classification, anomaly detection, and forecasting are essential for informed decision-making. Recently, large language models (LLMs) have gained prominence for their ability to handle complex data and extract meaningful insights. This study investigates whether LLMs are effective for time series data analysis by comparing their performance with non-LLM-based approaches across three tasks: classification, anomaly detection, and forecasting. Through a series of experiments using GPT4TS and autoregressive models, we evaluate their performance on benchmark datasets and assess their accuracy, precision, and ability to generalize. Our findings indicate that while LLM-based methods excel in specific tasks like anomaly detection, their benefits are less pronounced in others, such as forecasting, where simpler models sometimes perform comparably or better. This research highlights the role of LLMs in time series analysis and lays the groundwork for future studies to systematically explore their applications and limitations in handling temporal data.

Related papers

• Efficient Model Selection for Time Series Forecasting via LLMs [52.31535714387368]
  We propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection. Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-04-02T20:33:27Z)
• Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models [104.17057231661371]
  Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs) Their success depends on large, diverse, and high-quality datasets, which are challenging to build due to regulatory, diversity, quality, and quantity constraints. This survey provides a comprehensive review of synthetic data for TSFMs and TSLLMs, analyzing data generation strategies, their role in model pretraining, fine-tuning, and evaluation, and identifying future research directions.
  arXiv  Detail & Related papers  (2025-03-14T13:53:46Z)
• LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics [56.99021951927683]
  Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring. Existing Large Language Models (LLMs) usually perform suboptimally because they neglect the inherent characteristics of time series data. We propose LLM-PS to empower the LLM for TSF by learning the fundamental textitPatterns and meaningful textitSemantics from time series data.
  arXiv  Detail & Related papers  (2025-03-12T11:45:11Z)
• Large Language Models are Few-shot Multivariate Time Series Classifiers [23.045734479292356]
  Large Language Models (LLMs) have been extensively applied in time series analysis. Yet, their utility in the few-shot classification (i.e., a crucial training scenario) is underexplored. We aim to leverage the extensive pre-trained knowledge in LLMs to overcome the data scarcity problem.
  arXiv  Detail & Related papers  (2025-01-30T03:59:59Z)
• The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data [0.3749861135832072]
  This paper investigates and compares the performance of the mainstream LLMs, such as ChatGPT, PaLM, LLama, and Falcon, in generating deep learning models for analyzing time series data.<n>The results can be beneficial for data analysts and practitioners who would like to leverage generative AIs to produce good prediction models with acceptable goodness.
  arXiv  Detail & Related papers  (2024-11-27T20:18:36Z)
• Using Large Language Models for Expert Prior Elicitation in Predictive Modelling [53.54623137152208]
  This study proposes the use of large language models (LLMs) to elicit expert prior distributions for predictive models.<n>Our findings show that LLM-elicited prior parameter distributions significantly reduce predictive error compared to uninformative priors in low-data settings.<n>Prior elicitation also consistently outperforms and proves more reliable than in-context learning at a lower cost.
  arXiv  Detail & Related papers  (2024-11-26T10:13:39Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• Revisited Large Language Model for Time Series Analysis through Modality Alignment [16.147350486106777]
  Large Language Models have demonstrated impressive performance in many pivotal web applications such as sensor data analysis. In this study, we assess the effectiveness of applying LLMs to key time series tasks, including forecasting, classification, imputation, and anomaly detection. Our results reveal that LLMs offer minimal advantages for these core time series tasks and may even distort the temporal structure of the data.
  arXiv  Detail & Related papers  (2024-10-16T07:47:31Z)
• An Evaluation of Standard Statistical Models and LLMs on Time Series Forecasting [16.583730806230644]
  This study highlights the key challenges that large language models encounter in the context of time series prediction. The empirical results indicate that while large language models can perform well in zero-shot forecasting for certain datasets, their predictive accuracy diminishes notably when confronted with diverse time series data and traditional signals.
  arXiv  Detail & Related papers  (2024-08-09T05:13:03Z)
• A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting [45.0261082985087]
  We conduct a comprehensive evaluation of Large Language Models (LLMs) for temporal event forecasting. We find that directly integrating raw texts into the input of LLMs does not enhance zero-shot extrapolation performance. In contrast, incorporating raw texts in specific complex events and fine-tuning LLMs significantly improves performance.
  arXiv  Detail & Related papers  (2024-07-16T11:58:54Z)
• Review of Data-centric Time Series Analysis from Sample, Feature, and Period [37.33135447969283]
  A good time-series dataset is advantageous for the model's accuracy, robustness, and convergence. The emergence of data-centric AI represents a shift in the landscape from model refinement to prioritizing data quality. We systematically review different data-centric methods in time series analysis, covering a wide range of research topics.
  arXiv  Detail & Related papers  (2024-04-24T00:34:44Z)
• 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)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)

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.