ML based forecasting of CO2 series for various regions
1. USE THIS CODE AT CHECKOUT FOR BEST DEAL ( remove spaces ) : 1A0536C2E3 00D2A748A4
2. WHAT THIS COURSE IS ABOUT
1. ARIMA (AutoRegressive Integrated Moving Average) is a statistical modeling technique used for time-series forecasting, including the prediction of CO₂ emissions. At its core, ARIMA models capture patterns such as trends, seasonality, and autocorrelation within a given time series.
2. When using ARIMA for CO₂ emissions forecasting, the first step is to ensure that the emissions data is stationary. This involves differencing the data. After determining the orders of autoregression (p), differencing (d), and moving average (q) through diagnostic tools like the autocorrelation function (ACF) and partial autocorrelation function (PACF), analysts can fit an ARIMA model that captures the behavior of the CO₂ emission series.
3. Once the ARIMA model is properly fitted, it can generate short- to medium-term forecasts, giving crucial insights into expected emission levels. These forecasts can be used to guide decision-making on energy policies, carbon reduction strategies, and the allocation of resources for green technologies.
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