Deep Learning and Variational Modal Decomposition in Stock Price Prediction

This study explores stock price forecasting, a critical topic for economic stability and investor decision-making. Traditional models like ARIMA struggle with stock market complexity due to their linear assumptions. To address this, the study examines advanced methods, focusing on deep learning techniques such as CNNs and LSTMs for their predictive strengths. It proposes a hybrid model combining Variational Mode Decomposition (VMD) and Bi-directional Long Short-Term Memory Networks (BiLSTM). VMD reduces time series non-stationarity, while BiLSTM captures sequence features via bi-directional processing. Empirical results show the VMD-BiLSTM model outperforms others in RMSE, MAE, and R² metrics, achieving higher forecasting accuracy. Although performance decreases during extreme price fluctuations, it effectively captures main trends. This research highlights the practical value of deep learning in handling complex financial time series and provides innovative methods for stock price prediction.