Examining sea levels forecasting using autoregressive and prophet models

Abstract

Global climate change in recent years has resulted in significant changes in sea levels at both global and local scales. Various oceanic and climatic factors play direct and indirect roles in influencing sea level changes, such as temperature, ocean heat, and Greenhouse gases (GHG) emissions. This study examined time series analysis models, specifically Autoregressive Moving Average (ARIMA) and Facebook’s prophet, in forecasting the Global Mean Sea Level (GMSL). Additionally, Vector Autoregressive (VAR) model was utilized to investigate the influence of selected oceanic and climatic factors contributing to sea level rise, including ocean heat, air temperature, and GHG emissions. Moreover, the models were applied to regional sea level data from the Arabian Gulf, which experienced higher fluctuations compared to GMSL. Results showed the capability of autoregressive models in long-term forecasting, while the Prophet model excelled in capturing trends and patterns in the time series over extended periods of time.