climate warming prediction using time series analysis (case study: western iran)

One of the most important environmental challenges in today’s era is climatic changes and fluctuations. the phenomenon of global warming has always affected different parts of the world. therefore, it is necessary to investigate and predict the factors affecting it in various regions. in this study, the arima time series method was used to investigate the future temperature changes in the climate of aligoudarz plain in western iran. for this purpose, the monthly temperature information for the period of 1992-2023 was used from aligoudarz station. the maximum data for 1992 is equal to 13.27 and the minimum data for 2021 is equal to 6.78. to assess the specification of time series acf and pacf functions were used. the results showed that the time series is not stationary. therefore, the differentiation method was used for stationary. the time series after one-time differentiation was stationary, so the factor of d to 1. results of the time series investigation showed that after evaluating different arima models for the prediction of temperature, the model with the value of autocorrelation component=0, moving average=1, and differentiation=1 had the best result, so the forecasting was done with arima(0,1,1) model. the forecast results showed that in the next five years (2024-2028) the temperature will decrease. in general, the results of this study showed the acceptable effectiveness of the time series model in temperature forecasting.