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seasonal-trend decomposition architecture
A seasonal-trend decomposition architecture is a neural network design paradigm used in time series modeling and forecasting that integrates classical time series decomposition directly into the layers of a deep learning model. Under this architecture, intermediate representations or raw input sequences are dynamically separated into distinct trend-cyclical components that capture long-term directional shifts and seasonal components that capture repeating periodic patterns. By incorporating internal decomposition blocks, such as moving-average filters or frequency transforms, the network processes each decoupled component through specialized sub-modules before aggregating them into the final prediction. This structural separation simplifies the learning process, helping the model better isolate complex temporal dependencies, mitigate non-stationarity, and improve long-term forecasting accuracy.
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