Oil price prediction using dynamic multiresolution modeling

In this project, we will explore novel modeling methods to predict oil prices, based on a combination of machine learning methods with dynamic multiresolution analysis. The objective is to develop a software to better forecast oil prices. Oil is the worldâs leading fuel, and its prices have a big impact on the global environment, the economy as well as oil exploration and exploitation activities. Oil price forecasts are very useful to relevant industries, governments, and many individuals. Many methods have been developed for predicting oil prices. However, it remains one of the most challenging forecasting problems due to the high volatility of prices. In this project, we will explore novel modeling methods to predict oil prices, based on a combination of machine learning methods with dynamic multiresolution analysis. To evaluate and validate this model, we will compare the resulting forecasts with some popular oil price prediction models.

Faculty Supervisor:

Faramarz Samavati

Student:

Zeinab Hajiabotorabi

Partner:

1920525 Alberta Corporation

Discipline:

Computer science

Sector:

University:

Program:

Accelerate