Timer-XL: Long-Context Transformers for Unified Time Series Forecasting

Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long

Keywords: Time Series Forecasting, Transformer, Foundation Model

TL;DR: We introduce a generative Transformer for multi-dimensional time series, which achieves SOTA performance in supervised training and zero-shot forecasting.

Abstract: We present Timer-XL, a causal Transformer for unified time series forecasting. To uniformly predict multidimensional time series, we generalize next token prediction, predominantly adopted for 1D token sequences, to multivariate next token prediction. The paradigm formulates various forecasting tasks as a long-context prediction problem. We opt for decoder-only Transformers that capture causal dependencies from varying-length contexts for unified forecasting, making predictions on non-stationary univariate time series, multivariate series with complicated dynamics and correlations, as well as covariate-informed contexts that include exogenous variables. Technically, we propose a universal TimeAttention to capture fine-grained intra- and inter-series dependencies of flattened time series tokens (patches), which is further enhanced by deft position embedding for temporal causality and variable equivalence. Timer-XL achieves state-of-the-art performance across task-specific forecasting benchmarks through a unified approach. Based on large-scale pre-training, Timer-XL achieves state-of-the-art zero-shot performance, making it a promising architecture for pre-trained time series models. Code is available at this repository: https://github.com/thuml/Timer-XL.

Primary Area: learning on time series and dynamical systems

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Submission Number: 1290