Open AccessForecasting under applying machine learning and statistical models
Author(s) -
Azhari A. Elhag,
Hanaa Abu-Zinadah
Publication year - 2020
Publication title -
thermal science/thermal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.339
H-Index - 43
eISSN - 2334-7163
pISSN - 0354-9836
DOI - 10.2298/tsci20s1131e
Subject(s) - computer science , artificial intelligence , field (mathematics) , machine learning , series (stratigraphy) , statistical learning , statistical model , time series , econometrics , statistics , mathematics , paleontology , pure mathematics , biology
In a different area of a field of the real life, problem of accurate forecasting has acquired great importance that present the interesting serve which led to the best ways to achieve a goal. So, in this paper, we aimed to compare the accuracy of some statistical models such as Time Series and Deep Learning models, to forecasting the fertility rate in the Kingdom of Saudi Arabia, the data source is the World Health Organization over the period of 1960 to 2019. The performances of models were evaluated by errors measures mean absolute percentage error.