Optimised SARIMA models to forecast changes in ocean acidification and atmospheric CO2

Regression methods are widely used to model, smooth, and forecast datasets, whether in time series, scattered data, or logistic variables. Besides, linear regression is often preferred due to its simplicity and efficiency in identifying trends. However, many real-world phenomena, such as climate change, economic fluctuations, gambling, and biological processes, exhibit nonlinear behaviours that require more advanced regression techniques. For this reason, some studies include polynomial, exponential, and dynamic approaches derived from linear models. This study focuses on ocean acidification and carbon dioxide (CO2) emissions, two interconnected factors influencing climate change and marine ecosystems. Both datasets are structured as time series, where ocean acidification is measured in seawater pH levels, and CO2 emissions are recorded in parts per million. To perform a deep data analysis, this paper sets up a particular seasonal autoregressive integrated moving average model for prediction in order to process volatile time series with complex trends, providing, thus, a scalable and effective long-term forecasting technique.