@inproceedings{85642614ad0148b782084c4272330707,
title = "Predictive Analysis of Energy Use Based on Some Forecasting Models",
abstract = "A prediction method is a way to estimate a sequence of values based on time-series. This study aims to anticipate Saudi Arabia's energy use using autoregressive integrated moving average (ARIMA), Holt-Winters (H-W), and artificial neural network (ANN) models. This study also examines the accuracy of forecasting methods. The study forecasts energy use time-series data from 1971 to 2014 using statistical software. According to the results, ARIMA (2, 1, 2) is suitable for predicting the Kingdom of Saudi Arabia{\textquoteright}s energy usage in 2025. The findings of the study will assist government agencies in forecasting energy use.",
keywords = "Artificial Neural Networks (ANN), Autoregressive Integrated Moving Average (ARIMA), Energy use, Forecasting methods, Holt-Winters (H-W), Saudi Arabia",
author = "Ali AlArjani and Teg Alam",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th Annual Scientific International Conference for Business on Digital Economy and Business Analytics, SICB 2021 ; Conference date: 25-10-2021 Through 27-10-2021",
year = "2022",
doi = "10.1007/978-3-031-05258-3\_21",
language = "English",
isbn = "9783031052576",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "257--267",
editor = "Yaseen, \{Saad G.\}",
booktitle = "Digital Economy, Business Analytics, and Big Data Analytics Applications",
address = "Germany",
}