There is wide consensus among scientists and policymakers that global warming as defined by the Intergovernmental Panel on Climate Change (IPCC) should be pegged at 1.5 Celsius above the pre-industrial level of warming in order to maintain environmental sustainability . The threats and risks of climate change have been evident in the form of various extreme climate events, such as tsunamis, glacier melting, rising sea levels, and heating up of the atmospheric temperature. Emissions of greenhouse gases, such as carbon dioxide (CO2) are the main cause of global warming.
In this Project for Time Series Analysis and Forecasting Each step taken like EDA, Feature Engineering, Model Building, Model Evaluation and Prediction table, and Deployment. Explaining which model to select on basis of metrics like RMSE, MAPE and MAE value for each model. Finally explaining which model we will use for Forecasting.
Better accuracy in short-term forecasting is required for intermediate planning for the target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating organization to make informed decisions. Exponential techniques, Linear statistical modeling and Autoregressive models are used to forecast the emissions and the best model will be selected on these basis

• 1.) Minimum error
• 2.) Low bias and low variance trade off

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