ARCO1: An Application of Belief Networks to the Oil Market
This work addresses financial forecasting challenges for the oil industry, representing an incremental application of belief networks to a specific domain.
The paper tackles the problem of forecasting crude oil prices by applying belief networks to model market variables, resulting in the development of ARCO1, a flexible system that uses Monte Carlo analysis for predictions and facilitates team consensus.
Belief networks are a new, potentially important, class of knowledge-based models. ARCO1, currently under development at the Atlantic Richfield Company (ARCO) and the University of Southern California (USC), is the most advanced reported implementation of these models in a financial forecasting setting. ARCO1's underlying belief network models the variables believed to have an impact on the crude oil market. A pictorial market model-developed on a MAC II- facilitates consensus among the members of the forecasting team. The system forecasts crude oil prices via Monte Carlo analyses of the network. Several different models of the oil market have been developed; the system's ability to be updated quickly highlights its flexibility.