Analysis of Temperature Variability, Trends and Prediction in the Karachi Region of Pakistan Using ARIMA Models

Abstract

In this paper, the average monthly temperature of the Karachi region, Pakistan, has been modelled. The time period of the procured dataset is from January 1989 to December 2018. The Autoregressive Integrated Moving Average (ARIMA) modelling technique in conjunction with the Box–Jenkins approach has been applied to forecast the average monthly temperature of the study area. A total of 83.33% of the trained dataset is used for construction of the model, and the remaining 16.67% of the dataset is used for the validation of the model. The best-fitted model is identified as ARIMA (2, 1, 4), generated on the basis of minimum values of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) procedures. The accuracy parameters considered are Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Both parameters show that the model is 98.152% and 98.413% accurate, respectively. In addition, the Autoregressive Conditional Heteroscedasticity-Lagrange Multiplier (ARCH-LM) test has been conducted to check the presence of heteroscedasticity in the residuals of the identified model. This test shows no heteroscedasticity present in the residual series. By means of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, the most appropriate orders of the ARIMA model are determined and evaluated. The model has been employed to investigate the time series variables’ precise impact on the scale of the regional warming scenario. Accordingly, the created model can help in determining future strategies related to weather conditions in the Karachi region. From the forecast result, it is found that the average temperature seems to show an increasing trend. Such an increasing trend can potentially upset the weather conditions and economic activities of the coastal area of Pakistan.