Subventions et des contributions :

Titre :
PADD5 EIA Forecasting Using Machine Learning Techniques
Numéro de l’entente :
EGP
Valeur d'entente :
24 270,00 $
Date d'entente :
20 sept. 2017 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Colombie-Britannique, Autre, CA
Numéro de référence :
GC-2017-Q2-04403
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Nom légal du bénéficiaire :
Liu, Zheng (The University of British Columbia)
Programme :
Subventions d'engagement partenarial pour les universités
But du programme :

The support for optimal trading decision making is critical for the gasoline, jet and diesel product market. Thex000D
use of information from "stock levels" can help customers optimize their trading decisions before the market.x000D
Navarik, a Vancouver-based software company, is specialized in the oil and gas industry and has developed ax000D
Navarik Inspection platform to increase the speed and efficiency of trade settlement operations.x000D
In collaboration with Navarik, this research is to create the forecasting capabilities on the Navarik Inspectionx000D
platform and help Navarik to expand its business in trade settlement. The objective is to forecast stock levels inx000D
the PADD5 (Petroleum Administration for Defense Districts region 5) before the EIA (Energy Informationx000D
Administration) results are released. This research will develop the machine learning models by using thex000D
Navarik data, public data, and 3rd party data for stock level forecasting. The forecasting algorithms will bex000D
implemented on Microsoft Azure platform and incorporated into Navarik Inspection platform to serve itsx000D
customers and clients in the oil and gas trade market.