Temporal Pattern Intelligence: A Recurrent Neural Framework for Enhanced Financial Forecasting

Abstract

Accurate forecasting of financial time series remains a complex challenge due to asset price behaviour's non-stationary, nonlinear, and cyclical nature. While Long Short-Term Memory (LSTM) networks have shown promise in modeling sequential dependencies, they often struggle to capture periodic structures inherent in financial data. This study proposes a hybrid forecasting framework that integrates temporal pattern recognition techniques—specifically seasonal decomposition, wavelet transforms, and moving averages—into a recurrent neural architecture to improve predictive performance in cyclical markets. Using historical data from five representative financial instruments, the hybrid model enriches the LSTM input space with statistically significant temporal features, thereby enabling more comprehensive learning of both long-term dependencies and structural temporal patterns. Empirical results demonstrate that the proposed model significantly outperforms traditional LSTM baselines in terms of Root Mean Squared Error (RMSE), particularly in assets exhibiting strong cyclical behavior. The residual component from seasonal decomposition emerges as the most influential feature, reinforcing the importance of capturing irregular deviations in financial forecasting. This research contributes a structured and generalizable approach to combining temporal pattern recognition with deep learning, offering improved accuracy and interpretability for practitioners and researchers in computational finance