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Cited by 372Time Series Analysis and ForecastingEnergy Load and Power ForecastingStock Market Forecasting Methods

The DOI matches its Crossref record, with a small difference in title or year.

One Fits All: Power General Time Series Analysis by Pretrained LM

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Zhou, Tian, Niu, Peisong, Wang, Xue, Sun, Liang, Jin, Rong·2023·Advances in Neural Information Processing Systems 36
DOI 10.52202/075280-1877
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Abstract

Although we have witnessed great success of pre-trained models in natural language processing (NLP) and computer vision (CV), limited progress has been made for general time series analysis. Unlike NLP and CV where a unified model can be used to perform different tasks, specially designed approach still dominates in each time series analysis task such as classification, anomaly detection, forecasting, and few-shot learning. The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training. In this work, we address this challenge by leveraging language or CV models, pre-trained from billions of tokens, for time series analysis. Specifically, we refrain from altering the self-attention and feedforward layers of the residual blocks in the pre-trained language or image model. This model, known as the Frozen Pretrained Transformer (FPT), is evaluated through fine-tuning on all major types of tasks involving time series. Our results demonstrate that pre-trained models on natural language or images can lead to a comparable or state-of-the-art performance in all main time series analysis tasks, as illustrated in Figure 1. We also found both theoretically and empirically that the self-attention module behaviors similarly to principle component analysis (PCA), an observation that helps explains how transformer bridges the domain gap and a crucial step towards understanding the universality of a pre-trained transformer.The code is publicly available at https://github.com/DAMO-DI-ML/One_Fits_All.

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Metadata

Authors
Zhou, T., Niu, P., Wang, X., Sun, L., & Jin, R.
ORCID
Niu, Peisong
Venue
Advances in Neural Information Processing Systems 36
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Published
2023
Pages
43322-43355
ISSN
1049-5258 (ISSN-L)
DOI
10.52202/075280-1877
OpenAlex
W7133215398
Cited by
367
References
0
Citations by year
2026: 186 · 2025: 121 · 2024: 59 · 2023: 1
FWCI
76.22
Type
conference paper
Language
en
Open access
Licence
cc-by-nc-sa
Locations
  • neural information processing systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
Keywords
time series forecasting, pre-trained language models, power systems, time series classification

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