Stock Prediction with Stacked-LSTM Neural Networks

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 f...

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Bibliographic Details
Published in2021 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
LanguageEnglish
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
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Summary: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.
ISSN:2693-9371
DOI:10.1109/QRS-C55045.2021.00166