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WaveESN-RegimeMLP: GA-Tuned Reservoirs and Regime-Aware Multiscale Forecasting
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DOI：10.1007/s10614-026-11343-6.png)
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Abstract
En 中文
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.
Keywords:
Multiscale forecasting
MODWT
Echo state network
Elastic-net
Hidden Markov model
Genetic algorithm
Rolling-origin validation
Regime switching
Journal
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IF:
2.2
Papers:
255
Citations:
2.3K
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