Forecasting Based on Some Statistical and Machine Learning Methods

被引：5
|
作者：
Elhag, Azhari A. [1 ]
Almarashi, Abdullah M. [2 ]
机构：
[1] Taif Univ, Math & Stat Dept, At Taif 21974, Saudi Arabia
[2] King Abdulaziz Univ, Stat Dept, Jeddah 21589, Saudi Arabia
来源：
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING | 2020年 / 36卷 / 06期
关键词：
time series; modeling; deep learning; multilayer perceptron; forecasting;
D O I：
10.6688/JISE.202011_36(6).0002
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Forecasting consists basically of using data to predict the value of the attributes to promote micro- and macro-level decision making. There are many methods to do prediction extending from complexity and data requirement. In this paper, we present the method of an autoregressive integrated moving average (ARIMA), multilayer perceptron artificial neural network (ANN) model and decision tree (DT) method to forecast time-series data, also we use different methods to measure the accuracy of the forecasting of the patient dying after having Ebola virus in the Republic of Liberia over the period of 25 March 2014 to 13 April 2016. The data source is from World Health Organization (WHO).
引用
收藏
页码：1167 / 1177
页数：11
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