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

TL;DR

This paper demonstrates that pre-trained language and vision transformers can be effectively adapted for diverse time series analysis tasks without modification, achieving state-of-the-art results across multiple benchmarks.

Contribution

It introduces a method to leverage existing pre-trained models for time series analysis by using a frozen transformer, enabling universal application across tasks.

Findings

Pre-trained models achieve comparable or superior performance in time series tasks.

Self-attention in transformers behaves similarly to PCA, aiding domain adaptation.

The approach simplifies transfer learning for time series analysis.

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.