Reference number : GC-2017-Q2-04403, amendment number : , agreement type : G, recipient legal name : Liu, Zheng (The University of British Columbia), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The support for optimal trading decision making is critical for the gasoline, jet and diesel product market. The_x000D_
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 a_x000D_
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 Inspection_x000D_
platform and help Navarik to expand its business in trade settlement. The objective is to forecast stock levels in_x000D_
the PADD5 (Petroleum Administration for Defense Districts region 5) before the EIA (Energy Information_x000D_
Administration) results are released. This research will develop the machine learning models by using the_x000D_
Navarik data, public data, and 3rd party data for stock level forecasting. The forecasting algorithms will be_x000D_
implemented on Microsoft Azure platform and incorporated into Navarik Inspection platform to serve its_x000D_
customers and clients in the oil and gas trade market., program purpose french : The support for optimal trading decision making is critical for the gasoline, jet and diesel product market. The_x000D_
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 a_x000D_
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 Inspection_x000D_
platform and help Navarik to expand its business in trade settlement. The objective is to forecast stock levels in_x000D_
the PADD5 (Petroleum Administration for Defense Districts region 5) before the EIA (Energy Information_x000D_
Administration) results are released. This research will develop the machine learning models by using the_x000D_
Navarik data, public data, and 3rd party data for stock level forecasting. The forecasting algorithms will be_x000D_
implemented on Microsoft Azure platform and incorporated into Navarik Inspection platform to serve its_x000D_
customers and clients in the oil and gas trade market., agreement title english : PADD5 EIA Forecasting Using Machine Learning Techniques, agreement title french : PADD5 EIA Forecasting Using Machine Learning Techniques, agreement number : EGP, agreement value : 24270.0, agreement start date : 2017-09-20, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada