Forecasting the Prices of Crude-Oil, Natural-Gas and Refined Products
Understanding and predicting the prices of crucial energy commodities such as crude oil, natural gas, and refined products is essential for informed decision-making in the energy sector
- Course Schedule
Classroom Sessions:
| Date | Venue | Price |
|---|
02 - 06 Sep 2025
Online
$ 3,950
- Course Description
INTRODUCTION
Understanding and predicting the prices of crucial energy commodities such as crude oil, natural gas, and refined products is essential for informed decision-making in the energy sector. This course provides participants with the necessary knowledge and skills to navigate the complexities of forecasting these prices, enabling them to make strategic decisions and manage risks effectively.
WHY IT MATTERS
Accurate forecasting relies on robust data analysis. Energy markets are influenced by various factors, including geopolitical events, supply and demand dynamics, economic indicators, and environmental considerations. Analyzing historical and real-time data is critical for developing reliable forecasting models, enhancing decision-making, and adapting to market changes.
OBJECTIVES
- Equip participants with a comprehensive understanding of the factors influencing crude oil, natural gas, and refined product prices.
- Develop practical skills in data analysis and modeling for accurate price forecasting.
- Provide insights into the latest market trends and emerging factors affecting energy prices.
- Enable participants to develop effective risk management strategies based on forecasting outcomes.
WHO SHOULD ATTEND ?
This course is suitable for:
- Energy analysts and researchers
- Financial analysts in the energy sector
- Risk management professionals
- Traders and portfolio managers
- Government officials involved in energy policy
- Energy consultants and advisors
- Anyone seeking a deep understanding of energy market dynamics and price forecasting
- Course Outline
DAY 1
Fundamentals of Energy Markets
- Overview of global energy markets
- Factors influencing crude oil, natural gas, and refined product prices
- Historical trends and patterns in energy prices
DAY 2
Data Collection and Preprocessing
- Importance of data quality in forecasting
- Data sources and collection methods
- Preprocessing techniques for cleaning and transforming data
DAY 3
Statistical Models for Price Forecasting
- Introduction to statistical models (ARIMA, GARCH, etc.)
- Time series analysis for energy prices
- Model selection and validation techniques
DAY 4
Machine Learning Approaches
- Overview of machine learning in energy forecasting
- Regression models for price prediction
- Neural networks and deep learning applications
DAY 5
Risk Management and Scenario Analysis
- Developing risk models based on forecasting outcomes
- Scenario analysis for anticipating market changes
- Case studies and practical applications
- Integration of forecasting into strategic decision-making
- Options & Brochure