Stock Price Prediction in Indonesia's Mining Sector Using a Hybrid Conv1D-LSTM Model

Hamzah (Universitas Respati Yogyakarta)

International Journal of Informatics and Computation
Vol. 6 No. 1 (2024): International Journal of Informatics and Computation

This study presents a novel approach to forecasting stock prices in Indonesia's mining sector by leveraging a hybrid model combining Convolutional Neural Networks (Conv1D) and Long Short-Term Memory (LSTM) networks. Given the volatile nature of stock markets and the specific characteristics of the mining industry, accurate prediction models are essential for investors and analysts. The hybrid Conv1D-LSTM model integrates the feature extraction capabilities of Conv1D with the sequence learning strengths of LSTM, providing a robust framework for time series forecasting.