Stock Prediction with Stacked-LSTM Neural Networks

• Published in: 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C) pp. 1119 - 1125
• Main Authors: Zhang, Xiaochun, Li, Chen, Chen, Kuan-Lin, Chrysostomou, Dimitrios, Yang, Hongji
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2021
• Subjects: Deep Learning; Explosions; Neural networks; Over-fitting; Predictive models; Software quality; Software reliability; Stacked Long Short Term Memory; Time Series; Time series analysis; Training
• DOI: 10.1109/QRS-C55045.2021.00166

This paper explores a stacked long-term and short-term memory (LSTM) model for non-stationary financial time series in stock price prediction. The proposed LSTM is designed to overcome gradient explosion, gradient vanishing, and save long-term memory. Firstly, build time series with different days for network input, and then add early-stopping, rectified linear units (Relu) activation function to avoid over-fitting during the training stage. Finally, save trained parameters state and new batch size for testing. The results suggest that the developed stacked LSTM produces better predictive power and generalization.