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Forecasting Atmospheric CO2 Concentrations Using SARIMA Models: Insights from Time-Series Analytics

Year: 2025

Type: article

Abstract: This record contains the article "Forecasting Atmospheric CO2 Concentrations Using SARIMA Models: Insights from Time-Series Analytics" by Hemanth Kori. The paper analyses weekly CO2 concentrations at Mauna Loa Observatory (1958-2001) using a seasonal ARIMA model. The time series is decomposed into trend, seasonal and residual components, and the model is trained on 1958-1997 data and tested on 1998-2001. Forecasts achieve mean absolute error of about 1.07 ppm and RMSE of about 1.20 ppm, and two-year projections suggest levels above 370 ppm by the end of 2003. The study discusses the relevance of classical time-series methods amid emerging AI-driven analytics. Figures and code for the analysis are included.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16788044

Author: Kori, Hemanth

Language: English

Topic: Atmospheric and Environmental Gas Dynamics

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

OpenAlex ID: W6893430004

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