Machine learning-based time series models for effective CO2 emission prediction in India.

China, India, and the USA are the countries with the highest energy consumption and CO₂ emissions globally. As per the report of datacommons.org, CO₂ emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO₂ emission effect with the prediction of CO₂ emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO₂ emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO₂ emission prediction. [ABSTRACT FROM AUTHOR]