RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting

Financial time series forecasting presents structural challenges absent from standard benchmarks. Log-returns are non-stationary, exhibit exceptionally low signal-to-noise ratios, and follow regime-dependent temporal dependencies. Recent forecasting models typically rely on a fixed context window, which is mismatched with the time-varying optimal look-back of log-return processes. To address this limitation, we propose the Regime-Aware Variable-context Expert Network (RAVEN), a Mixture-of-Experts framework that adaptively determines the temporal context for each input sample. Specifically, RAVEN constructs a hierarchy of nested contiguous windows determined by the data itself. The model scores historical patches in reverse chronological order and applies Cumulative Importance Thresholding (CIT) to route each prefix to a scale-specialized expert. To overcome representation redundancy caused by overlapping inputs, it penalizes pairwise expert cosine similarity prior to aggregation. Meanwhile, a parallel global compression branch is incorporated to preserve macro-level temporal coherence. Experiments on cumulative log-return prediction (HS300, S&P500) and retail fund sales forecasting demonstrate that RAVEN achieves state-of-the-art performance, which translates into consistent risk-adjusted alpha gains in market backtests. Beyond financial applications, evaluations on four PEMS benchmarks confirm that its adaptive look-back generalizes effectively to broader physical dynamic systems.