A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model
- URL: http://arxiv.org/abs/2405.02358v2
- Date: Tue, 7 May 2024 01:59:37 GMT
- Title: A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model
- Authors: Jiexia Ye, Weiqi Zhang, Ke Yi, Yongzi Yu, Ziyue Li, Jia Li, Fugee Tsung,
- Abstract summary: Large language foundation models have unveiled their capabilities for cross-task transferability, zero-shot/few-shot learning, and decision-making explainability.
There are two main research lines, namely pre-training foundation models from scratch for time series and adapting large language foundation models for time series.
This survey offers a 3E analytical framework for comprehensive examination of related research.
- Score: 33.17908422599714
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Time series data are ubiquitous across various domains, making time series analysis critically important. Traditional time series models are task-specific, featuring singular functionality and limited generalization capacity. Recently, large language foundation models have unveiled their remarkable capabilities for cross-task transferability, zero-shot/few-shot learning, and decision-making explainability. This success has sparked interest in the exploration of foundation models to solve multiple time series challenges simultaneously. There are two main research lines, namely pre-training foundation models from scratch for time series and adapting large language foundation models for time series. They both contribute to the development of a unified model that is highly generalizable, versatile, and comprehensible for time series analysis. This survey offers a 3E analytical framework for comprehensive examination of related research. Specifically, we examine existing works from three dimensions, namely Effectiveness, Efficiency and Explainability. In each dimension, we focus on discussing how related works devise tailored solution by considering unique challenges in the realm of time series. Furthermore, we provide a domain taxonomy to help followers keep up with the domain-specific advancements. In addition, we introduce extensive resources to facilitate the field's development, including datasets, open-source, time series libraries. A GitHub repository is also maintained for resource updates (https://github.com/start2020/Awesome-TimeSeries-LLM-FM).
Related papers
- 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) - General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data [61.163542597764796]
We show that time series with different time granularities (or corresponding frequency resolutions) exhibit distinct joint distributions in the frequency domain.
A novel Fourier knowledge attention mechanism is proposed to enable learning time-aware representations from both the temporal and frequency domains.
An autoregressive blank infilling pre-training framework is incorporated to time series analysis for the first time, leading to a generative tasks agnostic pre-training strategy.
arXiv Detail & Related papers (2025-02-05T15:20:04Z) - Towards Generalisable Time Series Understanding Across Domains [10.350643783811174]
We introduce a novel pre-training paradigm specifically designed to handle time series heterogeneity.
We propose a tokeniser with learnable domain signatures, a dual masking strategy, and a normalised cross-correlation loss.
Our code and pre-trained weights are available at https://www.oetu.com/oetu/otis.
arXiv Detail & Related papers (2024-10-09T17:09:30Z) - 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) - TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis [29.232543319667005]
This work studies the problem of time series analysis with generalist (or foundation) models.
We consider the simple strategy of discretely tokenizing time series data drawn from a myriad of datasets via self-supervision.
Our method, TOkenized Time Series EMbeddings (TOTEM), produces such generalist time series models with minimal or no fine-tuning.
arXiv Detail & Related papers (2024-02-26T09:11:12Z) - MOMENT: A Family of Open Time-series Foundation Models [19.0845213853369]
We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis.
We compile a collection of public time series, called the Time series Pile, and systematically tackle time series-specific challenges.
We build on recent work to design a benchmark to evaluate time series foundation models on diverse tasks and datasets in limited supervision settings.
arXiv Detail & Related papers (2024-02-06T10:48:46Z) - 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 Models for Time Series and Spatio-Temporal Data: A Survey and
Outlook [95.32949323258251]
Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications.
Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
arXiv Detail & Related papers (2023-10-16T09:06:00Z) - Time Series Analysis via Network Science: Concepts and Algorithms [62.997667081978825]
This review provides a comprehensive overview of existing mapping methods for transforming time series into networks.
We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language.
Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.
arXiv Detail & Related papers (2021-10-11T13:33:18Z)
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.