Seasonal Autoregressive Integrated Moving Average for Climate Change Time Series Forecasting

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.