THERMAL SCIENCE

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Authors of this Paper

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Azhari A. Elhag

,

Hanaa Abu-Zinadah

FORECASTING UNDER APPLYING MACHINE LEARNING AND STATISTICAL MODELS

ABSTRACT
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.
KEYWORDS
time series, deep learning, fertility rate, statistical models, forecasting, error back propagation network, recurrent neural network, long short-term memory, root mean square error
PAPER SUBMITTED: 2020-05-10
PAPER REVISED: 2020-06-01
PAPER ACCEPTED: 2020-06-10
PUBLISHED ONLINE: 2020-10-25
DOI REFERENCE: https://doi.org/10.2298/TSCI20S1131E
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2020, VOLUME 24, ISSUE Supplement 1, PAGES [S131 - S137]
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Forecasting under applying machine learning and statistical models

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