Hierarchical orthogonal ESNs with adaptive signal decomposition for chaotic financial time series prediction

To improve multi-scale financial time series forecasting, still many challenges exist; specifically, the echo state networks (ESNs) are unable to involve non-stationary dynamics, chaotic behavior, and mixing of frequency components. This is critical concern in the field of fintech, where precise and sound forecasting of financial data is the core of risk management and making informed investment decisions. This work focused on such challenges and came up with a solution in the form of a Hierarchical Orthogonal Echo State Network (HO-ESN) with adaptive signal decomposition. This proposed HO-ESN model, consist of three main contributions, first, Variational Mode Decomposition (VMD), to break down raw financial signals into certain frequency components that isolate noise, regularities, and periodicities; second, orthogonal reservoir learning through singular value decomposition (SVD) to obtain near-orthogonal spectral properties to maximize memory capacity and stability with respect to edge of chaos; and third is hierarchical learning with mode-specific inner updates and mode-to-mode outer coordination. This model is trained and tested on four datasets, FinMultiTime (Russell 3000 constituents), RedStone Terminal (cryptocurrency high-frequency trades), TrueFX (EUR/USD tick data) and Fama-French factor portfolios. HO-ESN demonstrates a significant improvement in performance compared with state-of-the-art baselines such as standard ESN, LSTM, GRU, and Transformer models, with an increase in multi-step forecasting accuracy of up to 23%, directional accuracy of up to 19%, and reduces mode interference by up to 47%. Ablation experiments and scalability are used to validate the significance of each part and the scalability of the model to real-time financial forecasting.