CO₂ Emissions Forecasting with ARIMA in Python

Name: CO₂ Emissions Forecasting with ARIMA in Python
Rating: 4.8 (190 reviews)

Build Accurate Time Series Forecasts with Python - Energy Sector Application

Created byAlgorithmic Economist, PhD

Last updated 6/2026

English

What you'll learn

Build ARIMA models to forecast CO2 emissions using Python
Apply a proven 10-step methodology for creating statistically sound and reliable forecasts
Work with real World Bank data to analyze emissions trends for India, China, USA, UK, EU and global averages
Master essential statistical tests including overfitting analysis, naive model benchmarking, and sensitivity analysis
Quantify forecast uncertainty using confidence intervals and error metrics like MAPE
Create publication-ready visualizations of historical trends and future projections
Understand when ARIMA is appropriate for time series forecasting vs other methods
Implement best practices for model validation, hyperparameter tuning, and results interpretation

Course content

7 sections • 41 lectures • 4h 57m total length

Overview4:05
Introduction to the course
Additional Case Studies0:16
Ten step methodology4:33
Presentation of the 10-step methodology that is used for generating highly accurate forecasts.

Introduction9:15
Introduction to the section
Training the models17:03
How models are trained in Python
The JB test8:09
Implementing the JB test in Python
ARIMA training set predictions11:30
How to train the models in Python
Generating the test set predictions8:32
How to generate the test set predictions in Python
Test set errors5:06
calculating the test set errors in Python
Training set errors4:15
Calculating the training set errors in Python
Overfitting analysis6:30
Checking for overfitting in Python
Conducting the naive test13:51
Implementing the naive benchmark test
Sensitivity analysis versus hyperparameters10:29
Describing the difference between sensitivity analysis and hyperparameter analysis
Sensitivity analysis on test errors15:12
Conducting sensitivity analysis using Python
Theory on Forecasts11:25
Analysis of the theory for forecasts
Producing the forecasts14:08
In Python , presenting the entire process of forecast generation
Final selection of models11:53
Conducting the final model selection

Introduction0:01
Introduction to the section
The indicator dataset0:01
The dataset used in the analysis
Stationary series and kPSS7:42
Using the KPSS test for stationarity checks
Stationarity analysis12:49
Stationarity analysis
Differencing7:56
The process of differencing in Python
The forecast arima notebook
The notebook file
ACF and PACF plots8:07
Analysis of ACF and PACF plots
Auto ARIMA function8:30
Modelling the AUTO Arima function in python
Fitting the ARIMA models9:04
Fitting the models in Python
Inverting the differencing operation9:27
Inverting the differencing in Python
Training set predictions9:41
Generating predictions on the training set
Training and test set MAPE (error)3:30
Generating the training and test set errors
Overfitting analysis1:43
Implementing overfitting analysis in Python
Generating forecasts2:37
Generating forecasts in Python
Diagnostic tests9:51
Implementing diagnostic tests in python

Requirements

Absolute beginners welcome!
You'll receive the full Python code, which you can adjust to your own projects
No programming experience? Follow along and learn by doing
No statistics background needed
Just need a computer and enthusiasm

Description

WHO I AM: I hold a PhD from Imperial College London, where my research focused on power-system investment planning, energy economics and optimisation under uncertainty.

STUDENT BONUS: Note: Students who enroll in this course will receive 2 extra online courses for free. Visit Skool (the widely-known educational website), search for "Energy Data Scientist" and join for free.

What You'll Learn:

How to build an ARIMA model in Python that can forecast CO₂ emissions
How to achieve high accuracy in the forecasts that you will produce
How to work with World Bank historical data
How to implement advanced statistical tests
How to apply your model to real-world cases (India, China, USA, UK, European Union analysis)

Perfect For:

Environmental consultants and analysts
Energy economists and policy makers
Data scientists in sustainability
Climate professionals

Why This Matters:

With net-zero targets and mandatory carbon reporting, professionals who can produce credible emissions forecasts are in high demand. Master the skills that set you apart in the growing climate economy. Companies now require carbon footprint assessments for regulatory compliance and ESG reporting. Governments need emissions projections for policy planning. Consultancies charge premium rates for these capabilities. Whether you're advancing your current career or transitioning into sustainability, these practical forecasting skills open doors to roles paying $150,000-250,000+ in the rapidly expanding green economy.

Who this course is for:

Environmental/Climate Analysts seeking quantitative forecasting skills
Sustainability Professionals needing to project emissions for reporting
Energy Sector Professionals wanting data-driven analytical methods
Graduate Students & Researchers in environmental science, energy, or climate studies
Data Scientists/ML Engineers moving into climate and energy applications
ESG Analysts & Consultants requiring emissions projection capabilities
Policy Analysts working on climate strategies and carbon reduction plans
Anyone transitioning to climate tech who needs practical forecasting skills

CO₂ Emissions Forecasting with ARIMA in Python

What you'll learn

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Course content

Introduction3 lectures • 9min

Data Preprocessing4 lectures • 20min

Dataset Split3 lectures • 29min

ARIMA full implementation14 lectures • 2hr 27min

ARIMA Optimal Orders15 lectures • 1hr 31min

Conclusions1 lecture • 1min

BONUS1 lecture • 1min

Requirements

Description

Who this course is for: