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ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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Forecasting Atmospheric CO2 Concentrations Using SARIMA Models: Insights from Time-Series Analytics

Authors: Kori, Hemanth;

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

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green