Deep Learning for time-series forecasting on Carbon Dioxide

Use Machine Learning methodologies in Python - a step by step methodology for accurate forecasts

Created by: Research Lead - Dr. SGiannelos
Data Science, Optimization, ML applied to Energy

What Will I Learn?

• Understand and apply deep learning models for time-series forecasting of CO2 emissions using Python.
• Implement a step-by-step methodology for generating accurate CO2 forecasts, incorporating statistical tests and analysis.
• Analyze and forecast long-term carbon dioxide trends across different regions, including India, the USA, the UK, and more.
• Develop practical skills in data preprocessing, model validation, and performance optimization to create reliable environmental forecasts.

Requirements

• No prerequisites except basic Python.

Target audience

• Data scientists and analysts interested in applying deep learning techniques to environmental data and forecasting.
• Climate researchers and environmental professionals looking to enhance their skills in predictive modeling for CO2 emissions.
• Python programmers and developers eager to learn how to build time-series forecasting models using deep learning frameworks.
• Policy makers, energy analysts, and sustainability consultants who need accurate long-term CO2 forecasts to inform decision-making.
• Graduate students or academics in fields such as environmental science, data science, or machine learning, seeking practical applications in forecasting.