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American Journal of Business and Operations Research
Volume 8 , Issue 2, PP: 25-35 , 2023 | Cite this article as | XML | Html |PDF

Title

Seasonal Autoregressive Integrated Moving Average for Climate Change Time Series Forecasting

  Basant Sameh 1 * ,   Mahmoud Elshabrawy 2

1  Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
    (basant.sameh25@gmail.com)

2  Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
    (mahmoudelshabrawy662001@gmail.com)


Doi   :   https://doi.org/10.54216/AJBOR.080203

Received: May 19, 2022 Accepted: December 19, 2022

Abstract :

This study investigates the application of time series models, specifically ARIMA (Auto Regressive Integrated Moving Average) and SARIMAX (Seasonal Autoregressive Integrated Moving Average with eXogenous regressors), in the context of climate change. The ARIMA and SARIMAX models are mathematical methods that can be used to forecast future values of a time series related to climate change, taking into account trends and seasonality, as well as incorporating additional information through exogenous variables. The paper also delves into the mathematical foundations of the ARIMA and SARIMAX models, including the various operators used to eliminate trends, the use of lag polynomials to represent the autoregressive and moving average components of the model, and the incorporation of exogenous variables in the SARIMAX model. The study aims to provide a better understanding of the use of these models in analyzing and predicting the effects of climate change.

Keywords :

Forecasting; Time Series; ARIMA; SARIMAX; Climate Change

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Cite this Article as :
Style #
MLA Basant Sameh, Mahmoud Elshabrawy. "Seasonal Autoregressive Integrated Moving Average for Climate Change Time Series Forecasting." American Journal of Business and Operations Research, Vol. 8, No. 2, 2023 ,PP. 25-35 (Doi   :  https://doi.org/10.54216/AJBOR.080203)
APA Basant Sameh, Mahmoud Elshabrawy. (2023). Seasonal Autoregressive Integrated Moving Average for Climate Change Time Series Forecasting. Journal of American Journal of Business and Operations Research, 8 ( 2 ), 25-35 (Doi   :  https://doi.org/10.54216/AJBOR.080203)
Chicago Basant Sameh, Mahmoud Elshabrawy. "Seasonal Autoregressive Integrated Moving Average for Climate Change Time Series Forecasting." Journal of American Journal of Business and Operations Research, 8 no. 2 (2023): 25-35 (Doi   :  https://doi.org/10.54216/AJBOR.080203)
Harvard Basant Sameh, Mahmoud Elshabrawy. (2023). Seasonal Autoregressive Integrated Moving Average for Climate Change Time Series Forecasting. Journal of American Journal of Business and Operations Research, 8 ( 2 ), 25-35 (Doi   :  https://doi.org/10.54216/AJBOR.080203)
Vancouver Basant Sameh, Mahmoud Elshabrawy. Seasonal Autoregressive Integrated Moving Average for Climate Change Time Series Forecasting. Journal of American Journal of Business and Operations Research, (2023); 8 ( 2 ): 25-35 (Doi   :  https://doi.org/10.54216/AJBOR.080203)
IEEE Basant Sameh, Mahmoud Elshabrawy, Seasonal Autoregressive Integrated Moving Average for Climate Change Time Series Forecasting, Journal of American Journal of Business and Operations Research, Vol. 8 , No. 2 , (2023) : 25-35 (Doi   :  https://doi.org/10.54216/AJBOR.080203)