Time Series Analysis and Forecasting of Monthly Air Temperature Changes in Nairobi Kenya

Abstract

Climate change has for a long time been the biggest debate among many people all over the world. Temperature is a key element that can be used to detect climate change. Time series analysis and forecasting is one of the major tools used by scientists in meteorological and environmental fields to study phenomena like temperature, rainfall and humidity. The aim of this research is to build a time series SARIMA model and use this model to analyze and forecast the maximum and minimum air temperature of Nairobi City in order to inform stakeholders who depend directly or indirectly on it to plan in advance. The appropriate orders of models are picked based on the results of ACF and PACF plots and evaluated using the AIC criterion. The best forecasting SARIMA model for maximum temperature is (0, 0, 2) (0, 1, 1)12 and that for minimum temperature is (1, 0, 0) (0, 1, 1)12. The results show that the minimum temperature is gradually increasing over years supporting the fact that global warming is real.