TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting

• URL: http://arxiv.org/abs/2510.00461v2
• Date: Mon, 20 Oct 2025 14:49:33 GMT
• Title: TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
• Abstract summary: Time series can be decomposed into two factors, ie time-invariant and time-varying components, which indicate static and dynamic patterns, respectively.<n>We propose a lightweight static-dynamic decomposition framework, TimeEmb, for time series forecasting.<n>TimeEmb innovatively separates time series into two complementary components: (1) time-invariant component, captured by a novel global embedding module that learns persistent representations across time series, and (2) time-varying component, processed by an efficient frequency-domain filtering mechanism inspired by full-spectrum analysis in signal processing.
• Score: 17.139568952753883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal non-stationarity, the phenomenon that time series distributions change over time, poses fundamental challenges to reliable time series forecasting. Intuitively, the complex time series can be decomposed into two factors, \ie time-invariant and time-varying components, which indicate static and dynamic patterns, respectively. Nonetheless, existing methods often conflate the time-varying and time-invariant components, and jointly learn the combined long-term patterns and short-term fluctuations, leading to suboptimal performance facing distribution shifts. To address this issue, we initiatively propose a lightweight static-dynamic decomposition framework, TimeEmb, for time series forecasting. TimeEmb innovatively separates time series into two complementary components: (1) time-invariant component, captured by a novel global embedding module that learns persistent representations across time series, and (2) time-varying component, processed by an efficient frequency-domain filtering mechanism inspired by full-spectrum analysis in signal processing. Experiments on real-world datasets demonstrate that TimeEmb outperforms state-of-the-art baselines and requires fewer computational resources. We conduct comprehensive quantitative and qualitative analyses to verify the efficacy of static-dynamic disentanglement. This lightweight framework can also improve existing time-series forecasting methods with simple integration. To ease reproducibility, the code is available at https://github.com/showmeon/TimeEmb.

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