A progressive decomposition architecture is a neural network design for time series forecasting that integrates series decomposition directly into the internal layers of a deep learning model rather than applying it solely as an initial preprocessing step. Within this framework, complex temporal signals and their intermediate feature representations are iteratively separated across successive layers into distinct underlying components, typically isolating long-term trend-cyclical movements from periodic seasonal fluctuations. By repeatedly decoupling and refining these patterns throughout multiple stages of the network, the architecture reduces interference between conflicting temporal scales, simplifies the discovery of long-range dependencies, and allows specialized model components to handle different temporal characteristics more effectively.
