Integrated computational intelligence for forecasting import and export volumes of commodities

The global seaborne trade is growing rapidly and being supported by shipping, one of the major transportation tools around the world. With the satellite and terrestrial AIS (Automatic Identification System) data, it is possible to track the trajectories of vessels carrying commodities. The capability to accurately forecast the import and export volumes and types of commodities will potentially enable the maximization of business trading profits. This research aims to develop forecasting algorithms to predict the trading in the business with the vessel tracking and cargo inspection data. Integrated with industry data analytic platform, the outcome from this research will enhance Canadian business competency in the global market.

Faculty Supervisor:

Zheng Liu

Student:

Usama Ansari

Partner:

Spire Luxembourg

Discipline:

Engineering - other

Sector:

Other

University:

Program: