Extraction of Technical Indicators and Data Augmentation-Based Stock Market Prediction Using Deep LSTM Integrated Competitive Swarm Feedback Algorithm

Abstract

In recent decades, time-series analysis and value forecasting have grown in research fields. Making a significant decision in the stock market prediction strategy requires knowledge that may be gained by forecasting with time-series data. Various prediction methods are developed to forecast future stock prices, but accurate prediction with the time-series data using external factors still results in a difficult task. An effective prediction approach is designed in this paper using the adopted Competitive Swarm Feedback Algorithm-based Deep Long Short-Term Memory (CSFA-based Deep LSTM) classifier for predicting the stock market data. The CSFA is the integration of the Competitive Swarm Optimizer (CSO) and Feedback Artificial Tree (FAT) algorithm. Then, technical indicators’ extraction, feature fusion and data augmentation steps are carried out. The Deep LSTM achieved higher prediction results than the other traditional classifiers. The proposed method achieved lower MAE, lower MSE, and minimum RMSE with the values of 0.1418, 0.1119, and 0.2557.

Multi-Feature Supervised Reinforcement Learning for Stock Trading

“…It has the characteristics of parameter sharing, so it is suitable for dealing with the problem of long-cycle time series prediction, and the prediction speed is fast, and the accuracy is high. Therefore, LSTM forecasting method has been widely used in many fields such as weather forecasting, stock forecasting, and behavior forecasting [20], [21]. In the aspect of stock forecasting, many scholars have confirmed the superiority of LSTM.…”