Analyzing Long-Term Records of Global Average Sea Level Change Using ARIMA Model

The purpose of this study was to demonstrate the role of time series model in predicting process and to pursue analysis of time series data using long-term records of global average sea level change from 1880 to 2013 extracted from the U.S. Environmental Protection Agency using data from Commonwealth Scientific and Industrial Research Organization, 2015. Following the Box–Jenkins method, ARIMA(0,1,1,) model was the best fitted model in prediction for the data, Global Average Absolute Sea Level Change, 1880-2013, in this study. Forecasting process with ARIMA(0,1,1) model for prediction indicated global average sea level change at a constant increasing rate in the short-term. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to significantly improve our understanding and narrow projections of future sea level rise and variability.