Timer-XL: Long-Context Transformers for Unified Time Series Forecasting
Keywords: Time Series Forecasting, Transformer
TL;DR: We extend next token prediction for multivariate time series, presenting a generative Transformer for various forecasting scenarios, which achieves the SOTA in diverse benchmarks and shows powerful strength as a one-for-all forecaster.
Abstract: We present Timer-XL, a generative Transformer for unified time series forecasting. To uniformly predict 1D and 2D time series, we generalize next token prediction, predominantly adopted for causal generation of 1D sequences, to multivariate next token prediction. The proposed paradigm uniformly formulates various forecasting scenarios as a long-context generation problem. We opt for the generative Transformer, which can capture global-range and causal dependencies while providing contextual flexibility, to implement unified forecasting on univariate series characterized by non-stationarity, multivariate time series with complicated dynamics and correlations, and covariate-informed contexts that include both endogenous and exogenous time series. Technically, we propose a universal TimeAttention to facilitate generative Transformers on multiple time series, which can effectively capture fine-grained intra- and inter-series dependencies of flattened time series tokens (patches) and is further strengthened by position embeddings in both temporal and variable dimensions. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach. By pre-training on large-scale time series, Timer-XL demonstrates notable zero-shot performance, making it a promising architecture for large time series models.
Primary Area: learning on time series and dynamical systems
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Submission Number: 1290
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