An integration of discrete wavelet transform and time-series recurrent neural network to improve stock price prediction
TL;DR
It is demonstrated that a streamlined, efficient model can outperform complex “deep” architectures when paired with robust data-cleaning techniques, and challenges the common belief that “deeper is better” in stock market forecasting.
Abstract
Predicting stock prices is notoriously difficult because financial data is noisy and non-linear. This study proposes a model designed to overcome common limitations in both data quality and predictive architecture. We propose a framework that combines two methods. First, we use a Discrete Wavelet Transform (DWT) to clean the data, separating the underlying trends from market noise. Second, we feed this denoised data into a time-series recurrent neural network (TRNN). We optimized the TRNN by implementing the Swish activation function, which helps the model learn more effectively and mitigates the vanishing gradient problem. The model was validated using historical data from the Dow Jones Industrial Average (DJIA). Our results show that a single-hidden-layer network performs significantly better than deeper and more complex networks. Deeper models perform poorly because they overfit clean data. Activation function was the most important factor for the success of this study. The Swish function allowed the model to learn effectively, whereas the older functions did not. The final best model had a very low error rate of 0.71%. The proposed model achieves a 0.71% error rate, representing a 13-fold improvement over previous benchmarks. This study demonstrates that a streamlined, efficient model can outperform complex “deep” architectures when paired with robust data-cleaning techniques. These findings challenge the common belief that “deeper is better” in stock market forecasting.