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<p>Financial series change their behavior over time and contain noise at many scales, which weakens standard linear forecasts. We present WaveESN–RegimeMLP, a modular pipeline that combines (i) wavelet-based multiscale features, (ii) a reservoir network with an elastic-net readout, (iii) a hidden-state model that detects market regimes from residuals, and (iv) genetic search for hyperparameters. Evaluated on daily prices of AAPL, DIA, SPY, and JPM from 1 January 2021 to 1 January 2022, the approach reduces one-day-ahead RMSE by 10–37% and z-normalized RMSE by 10–32% relative to a standard reservoir baseline. Gains are largest during volatile periods with frequent regime switches. The design is transparent, computationally light, and readily extendable to more assets and horizons.</p>

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WaveESN–RegimeMLP: GA-Tuned Reservoirs and Regime-Aware Multiscale Forecasting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月21日

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  • Çağlar Sözen

摘要

Financial series change their behavior over time and contain noise at many scales, which weakens standard linear forecasts. We present WaveESN–RegimeMLP, a modular pipeline that combines (i) wavelet-based multiscale features, (ii) a reservoir network with an elastic-net readout, (iii) a hidden-state model that detects market regimes from residuals, and (iv) genetic search for hyperparameters. Evaluated on daily prices of AAPL, DIA, SPY, and JPM from 1 January 2021 to 1 January 2022, the approach reduces one-day-ahead RMSE by 10–37% and z-normalized RMSE by 10–32% relative to a standard reservoir baseline. Gains are largest during volatile periods with frequent regime switches. The design is transparent, computationally light, and readily extendable to more assets and horizons.