Open AccessApplication of LSTM and CONV1D LSTM Network in Stock Forecasting Model
Author(s) -
Qiaoyu Wang,
Kai Kang,
Zhihan Zhang,
Donglin Cao
Publication year - 2021
Publication title -
artificial intelligence advances
Language(s) - English
Resource type - Journals
ISSN - 2661-3220
DOI - 10.30564/aia.v3i1.2790
Subject(s) - computer science , stock market , stock market prediction , artificial neural network , stock (firearms) , machine learning , artificial intelligence , econometrics , time series , long short term memory , recurrent neural network , regression , financial market , economics , finance , statistics , mathematics , engineering , mechanical engineering , paleontology , horse , biology
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.