Enhancing Recurrent Neural Networks for Stock Market Forecasts Through Pec-W Framework

It is difficult to predict stock prices and many indicators are used for this purpose. This difficulty is even greater in short-term transactions. Regardless of the term, it should definitely be used for buying and selling timing. In this study, we propose an approach to predict this timing. This paper addresses challenges in time series forecasting using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, commonly employed in stock market forecasting, such as overfitting and extended learning times. To mitigate these issues, the PEC-W preprocessing framework, generally adapted from Convolutional Neural Networks (CNN), is applied to enhance forecasting accuracy without altering model parameters. This approach incorporates normalization and data augmentation to prevent overfitting, and utilizes Discrete Wavelet Transform (DWT) to reduce learning times while preserving temporal characteristics. Aggregation through averaging and mean subtraction further improves data visibility and model accuracy. The effectiveness of the PEC-W technique is validated using Explainable Artificial Intelligence (XAI) method, such as SHAP, which confirm the robustness of the enhanced forecasting approach.