Recent advances in transformer architectures have significantly improved performance in time‐series forecasting. Despite the excellent performance of attention mechanisms in global modeling, they often overlook local correlations between seasonal cycles. Drawing on the idea of trend‐seasonality decomposition, we design a seasonal cycle‐aware time‐series forecasting model (SCA‐Net). This model uses a dual‐branch extraction architecture to decompose time series into seasonal and trend components, modeling them based on their intrinsic features, thereby improving prediction accuracy and model interpretability. We propose a method combining global modeling and local feature extraction within seasonal cycles to capture the global view and explore latent features. Specifically, we introduce a frequency‐domain attention mechanism for global modeling and use multiscale dilated convolution to capture local correlations within each cycle, ensuring more comprehensive and accurate feature extraction. For simpler trend components, we apply a regression method and merge the output with the seasonal components via residual connections. To improve seasonal cycle identification, we design an adaptive decomposition method that extracts trend components layer by layer, enabling better decomposition and more useful information extraction. Extensive experiments on eight classic datasets show that SCA‐Net achieves a performance improvement of 12.1% in multivariate forecasting and 15.6% in univariate forecasting compared to the baseline.

In recent years, time series forecasting models based on the Transformer framework have shown great potential, but they suffer from the inherent drawback of high computational complexity and only focus on global modeling. Inspired by trend-seasonality decomposition, we propose a method that combines global modeling with local feature extraction within the seasonal cycle. It aims at capturing the global view while fully exploring the potential features within each seasonal cycle and better expressing the long-term and periodic characteristics of time series. We introduce a frequency domain parity correction block to compute global attention and utilize multi-scale dilated convolution to extract local correlations within each cycle. Additionally, we adopt a dual-branch structure to separately model the seasonality and trend based on their intrinsic features, improving prediction performance and enhancing model interpretability. This model is implemented on a completely single-layer decoder architecture, breaking through the traditional encoder-decoder architecture paradigm and reducing computational complexity to a certain extent. We conducted sufficient experimental validation on eight benchmark datasets, and the results demonstrate its superior performance compared to existing methods in both univariate and multivariate forecasting.