Predicting Stock Market Price: A Logical Strategy using Deep Learning

被引：6

作者：

Biswas, Milon ^{[1
]}

Shome, Atanu ^{[2
]}

Islam, Md Ashraful ^{[1
]}

Nova, Arafat Jahan ^{[1
]}

Ahmed, Shamim ^{[1
]}

机构：

[1] Bangladesh Univ Business & Technol, Comp Sci & Engn, Dhaka, Bangladesh

[2] Khulna Univ, Comp Sci & Engn, Khulna, Bangladesh

来源：

11TH IEEE SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS (ISCAIE 2021) | 2021年

关键词：

Stock Market Prediction; LSTM; XGBoost; Linear Regression; Moving Average; Last Value Model; Machine Learning; Deep Learning;

D O I：

10.1109/ISCAIE51753.2021.9431817

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In time series data analysis, stock market prediction is particularly hard. In addition, for the best estimation of stock prices, proper tuning of the model is crucial. This research work uses the frequently used algorithms Long Short Term Memory, Extreme Gradient Boosting (XGBoost), Linear Regression, Moving Average, and Last Value model on more than twelve months of historical stock data to build up a prediction model for forecasting stock price. For the purpose of comparing among the models, the measurement of Mean Absolute Percentage Error (MAPE) is used and it is observed that the LSTM method exceeds all the other methods with a MAPE of 0.635. Furthermore, the highest error rate among the five models is found for Moving Average for our case.

引用

页码：218 / 223

页数：6

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