Timer: Generative Pre-trained Transformers Are Large Time Series Models

TL;DR

This paper introduces Timer, a large pre-trained transformer model for time series analysis, capable of handling forecasting, imputation, and anomaly detection through a unified generative approach, leveraging large-scale datasets and GPT-style architecture.

Contribution

It pioneers the development of large time series models (LTSMs) using pre-training on massive datasets and a unified generative framework, extending GPT-style models to time series tasks.

Findings

Timer demonstrates strong performance across multiple time series tasks.

Pre-training on large datasets improves model generalization.

Unified generative approach simplifies multi-task learning.

Abstract

Deep learning has contributed remarkably to the advancement of time series analysis. Still, deep models can encounter performance bottlenecks in real-world data-scarce scenarios, which can be concealed due to the performance saturation with small models on current benchmarks. Meanwhile, large models have demonstrated great powers in these scenarios through large-scale pre-training. Continuous progress has been achieved with the emergence of large language models, exhibiting unprecedented abilities such as few-shot generalization, scalability, and task generality, which are however absent in small deep models. To change the status quo of training scenario-specific small models from scratch, this paper aims at the early development of large time series models (LTSM). During pre-training, we curate large-scale datasets with up to 1 billion time points, unify heterogeneous time series into single-series sequence (S3) format, and develop the GPT-style architecture toward LTSMs. To meet diverse application needs, we convert forecasting, imputation, and anomaly detection of time series into a unified generative task. The outcome of this study is a Time Series Transformer (Timer), which is generative pre-trained by next token prediction and adapted to various downstream tasks with promising capabilities as an LTSM. Code and datasets are available at: https://github.com/thuml/Large-Time-Series-Model.