Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Qiaoyu Wang (Master of Professional Electrical Engineering, 24/03/1996, Monash University)
Kai Kang (Master of Professional Electrical Engineering, 28/04/1996, Monash University)
Zhihan Zhang (Master of Professional Electrical Engineering, 23/12/1996, Monash University)
Demou Cao (Master of Professional Electrical Engineering, 08/01/1995, Monash University)

Article ID: 2790

DOI: https://doi.org/10.30564/aia.v3i1.2790

Abstract


Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory.


Keywords


Linear regression;Polynomial regression;Long short-term memory network;One dimensional convolutional neural network

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