ARIMA Machine Learning- timeseries forecasts. CO2 case study
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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
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IntroductionIntroductory lecture for this section
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More Resources
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Data Preprocessing in PythonImplementing Data Preprocessing in Python
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Download ARIMA paper
An example ARIMA paper
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Download the code for this section
Click to download the attached file
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Download all the datasets
These are the datasets used in the video lecture.
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Introduction
Introduction to the section
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Training the models
How models are trained in Python
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The JB test
Implementing the JB test in Python
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ARIMA training set predictions
How to train the models in Python
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Generating the test set predictions
How to generate the test set predictions in Python
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Test set errors
calculating the test set errors in Python
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Training set errors
Calculating the training set errors in Python
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Overfitting analysis
Checking for overfitting in Python
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Conducting the naive test
Implementing the naive benchmark test
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Sensitivity analysis versus hyperparameters
Describing the difference between sensitivity analysis and hyperparameter analysis
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Sensitivity analysis on test errors
Conducting sensitivity analysis using Python
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Theory on Forecasts
Analysis of the theory for forecasts
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Producing the forecasts
In Python , presenting the entire process of forecast generation
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Final selection of models
Conducting the final model selection
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Introduction
Introduction to the section
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The indicator dataset
The dataset used in the analysis
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Stationary series and kPSS
Using the KPSS test for stationarity checks
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Stationarity analysis
Stationarity analysis
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Differencing
The process of differencing in Python
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The forecast arima notebook
The notebook file
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ACF and PACF plots
Analysis of ACF and PACF plots
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Auto ARIMA function
Modelling the AUTO Arima function in python
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Fitting the ARIMA models
Fitting the models in Python
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Inverting the differencing operation
Inverting the differencing in Python
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Training set predictions
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Training and test set MAPE (error)
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Overfitting analysis
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Generating forecasts
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Diagnostic tests
