ResDIF: A Residual Disentanglement Framework for Interpretable Financial Time Series Forecasting via Spectrally-Enhanced Temporal Encoding

Financial time series forecasting tasks, specifically stock trend prediction and market attribution, are essential for quantitative investment and risk control. However, these tasks suffer from low signal-to-noise ratios and non-stationarity, stemming from the coupling of hierarchical drivers: market trends, sector rotations, and idiosyncratic dynamics. Existing methods, often relying on static sector labels or time-domain correlations, struggle to capture dynamic, multi-scale dependencies and lack the interpretability required for return attribution. To address this, we propose ResDIF, a Residual Disentanglement framework for Interpretable financial time series Forecasting inspired by Asset Pricing Theory (APT). ResDIF employs a progressive residual architecture via a Spectrally-Enhanced Temporal Encoding mechanism to explicitly decompose stock data into market, sector, and individual layers. Furthermore, a self-supervised orthogonal loss encourages feature separation, enabling the model to autonomously decouple systematic risks from idiosyncratic alpha. Beyond predictive modeling, ResDIF effectively unifies high predictive accuracy with granular structural attribution. By leveraging this intrinsic interpretability to quantify structural market risks, our framework further enables adaptive portfolio optimization through dynamic hedging and asset selection. Experiments demonstrate that ResDIF outperforms existing methods while providing actionable interpretability support for refined portfolio risk management.