ARIMA Machine Learning- timeseries forecasts. CO2 case study

ARIMA Machine Learning- timeseries forecasts. CO2 case study

ML based forecasting of CO2 series for various regions

What you'll learn

• TO GET ALL EXTRAS PLEASE USE THIS code at checkout (remove spaces): EC59B6443 5AFD890BAC0
• REMEMBER: All the code, is available for you to download! Plus: publications and tutorials!
• YOU WILL LEARN how ARIMA machine learning is implemented and used for time series forecasting.
• ALWAYS EXPANDING: New videos and publications are added every 6–12 months, so be sure to check back!
• FAST HELP WITHIN HOURS: Have questions or need guidance? Send a message and get a response within hours!

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  - You will get the course + 10 publications (related to ARIMA methods). After purchase, just send me a private message here.

2. Course Overview:

1. ARIMA (AutoRegressive Integrated Moving Average) is a widely used statistical modeling technique for time-series forecasting, including the prediction of CO₂ emissions. 

It captures key patterns in the data, such as trends, seasonality, and autocorrelation, allowing for a structured approach to forecasting. 

ARIMA is particularly useful when historical emission data follows a consistent pattern that can be extrapolated into the future.

2. When applying ARIMA to CO₂ emissions forecasting, the first step is ensuring that the emissions data is stationary, meaning its statistical properties (mean, variance, and autocorrelation) remain constant over time. 

This often requires differencing the data to remove trends and seasonal effects. 

The appropriate orders of autoregression (p), differencing (d), and moving average (q) are determined using diagnostic tools such as the autocorrelation function (ACF) and partial autocorrelation function (PACF). 

Once these parameters are selected, analysts can fit an ARIMA model that effectively represents the dynamics of CO₂ emissions**.

3. A well-fitted ARIMA model can generate short- to medium-term forecasts, offering valuable insights into expected emission trends. 

These forecasts can help policymakers, researchers, and businesses make informed decisions about energy policies, carbon reduction strategies, and investment in sustainable technologies. 

Additionally, ARIMA models can be extended to SARIMA (Seasonal ARIMA) to better handle emissions data with strong seasonal patterns. 

Regularly updating the model with new data ensures that forecasts remain accurate and relevant in a rapidly changing environmental landscape.

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Your Instructor

Dr SpyRos