doi: 10.7763/IJET.2024.V16.1267
Forecasting Carbon Emission Using ETS Exponential Smoothing, ARIMA and Regression with ARIMA errors Techniques
Jiali Feng
1,*
,
- 1 University of Malaya, Kuala Lumpur, Malaysia
- 2 Guangdong University of Finance & Economics, Guangzhou, China
- Manuscript received March 13, 2024
- revised April 17, 2024
- accepted May 21, 2024
- published July 11, 2024
Abstract
Forecasting Carbon Emissions Using Time Series Analysis Global warming is one of the most difficult and complex problems facing the world today, and forecasting carbon emissions has become a worldwide challenge. In this study, we try to use three models Exponential Smoothing (ETS) model, seasonal ARIMA and error regression Autoregressive Integrated Moving Average (ARIMA) model to train the data of carbon dioxide emissions in a region of the United States from 1990 to 2015, to simulate and forecast the carbon emissions in the United States, and to find out the optimal forecasting model.
Keywords
- carbon emissions
- Exponential Smoothing (ETS)
- Autoregressive Integrated Moving Average (ARIMA)
- Seasonal Autoregressive Integrated Moving Average (SARIMA)
- ARIMA with error
How to Cite
Jiali Feng and Baoli Huang, "Forecasting Carbon Emission Using ETS Exponential Smoothing, ARIMA and Regression with ARIMA errors Techniques," International Journal of Engineering and Technology, vol. 16, no. 3, pp. 125-129, 2024. https://doi.org/ 10.7763/IJET.2024.V16.1267
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).