Climate Change Prediction Using ARIMA Model

Abstract

Abstract: It is a challenging task to forecast weather data accurately. The temperature change has important implications for business and economic activity. Effective management of global climate change impacts will depend upon accurate and costeffective forecasts. This paper univariate statistic techniques to model the properties of a world mean temperature dataset to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon and the ARIMAbased prognostication tool has been developed by implementing the ARIMA algorithm in python. Although the model is estimated on global temperature data, the methodology could even be applied to temperature data at more localized levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks as well as ARIMA and SARIMA modelling. This paper helps us to predict the air temperature, which is the main problem of global warming. Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in Tamilnadu. Time-series techniques to develop a parsimonious model of global mean temperature change that can be used to forecast over the short-term horizon (5- 10) years. Keywords: Global warming, Forecasting, temperature