Future of Information and Communication Conference (FICC) 2021
29-30 April 2021
Publication Links
IJACSA
Special Issues
Article Details
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
ARIMA Model for Accurate Time Series Stocks Forecasting
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110765
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 7, 2020.
Abstract: With the increasing of historical data availability and the need to produce forecasting which includes making decisions regarding investments, in addition to the needs of developing plans and strategies for the future endeavors as well as the difficulty to predict the stock market due to its complicated features, This paper applied and compared auto ARIMA (Auto Regressive Integrated Moving Average model). Two customize ARIMA(p,D,q) to get an accurate stock forecasting model by using Netflix stock historical data for five years. Between the three models, ARIMA (1,1,33) showed accurate results in calculating the MAPE and holdout testing, which shows the potential of using the ARIMA model for accurate stock forecasting.
Keywords: ARIMA; forecasting; prediction analysis; time series; stocks forecasting; data mining; big data
Shakir Khan and Hela Alghulaiakh, “ARIMA Model for Accurate Time Series Stocks Forecasting” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110765
@article{Khan2020,
title = {ARIMA Model for Accurate Time Series Stocks Forecasting},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110765},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110765},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Shakir Khan and Hela Alghulaiakh}
}
