Reference number : GC-2017-Q4-00098, amendment number : , agreement type : G, recipient legal name : Badescu, Andrei Lucian (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Demand forecasting has always been important for retailers as it drives important decisions regarding business_x000D_
strategy and supply chain management. Traditional approaches model the purchase arrival rates using classical_x000D_
statistical models, which were developed without easy access to scalable computing power and both wide_x000D_
(many types of data) and long (for many individuals) purchase data. Benefits brought forth by the huge influx_x000D_
of data during the recent years ought not to be limited to having better estimates of aggregate statistics to apply_x000D_
classical statistical models, but should also include a more fine-grained modelling based on the detailed_x000D_
individual-level purchase information._x000D_
There have been many models proposed in classical statistics to describe the behaviour of event arrivals, as_x000D_
they're useful in many areas, such as monitoring equipment remaining lifetimes and disaster forecasting. These_x000D_
models have been meticulously described and their properties have been rigorously proven. However, these_x000D_
same models also require a large number of assumptions, which may not necessarily hold in reality. One such_x000D_
assumption is that the dependence between events occurring further apart in time also tends to be smaller. We'd_x000D_
like to explore a data-driven model that aims to relax these assumptions. The recent explosion in accessibility_x000D_
to computational power has re-ignited interest in the Artificial Neural Net, which has shown success in many_x000D_
different areas of application. A modification to this is the Recurrent Neural Net. With the addition of an_x000D_
internal state variable, this neural-network set-up can be applied to sequential data as well. It had also been_x000D_
shown to be Turing complete, meaning that it can emulate any computer program given a large enough set of_x000D_
parameters. Applications of the RNN in natural language processing have been rather successful,_x000D_
demonstrating that this model is much better than classical statistical models in capturing complex sequential_x000D_
dependence, since the classical models require short-range dependence assumptions to work., program purpose french : Demand forecasting has always been important for retailers as it drives important decisions regarding business_x000D_
strategy and supply chain management. Traditional approaches model the purchase arrival rates using classical_x000D_
statistical models, which were developed without easy access to scalable computing power and both wide_x000D_
(many types of data) and long (for many individuals) purchase data. Benefits brought forth by the huge influx_x000D_
of data during the recent years ought not to be limited to having better estimates of aggregate statistics to apply_x000D_
classical statistical models, but should also include a more fine-grained modelling based on the detailed_x000D_
individual-level purchase information._x000D_
There have been many models proposed in classical statistics to describe the behaviour of event arrivals, as_x000D_
they're useful in many areas, such as monitoring equipment remaining lifetimes and disaster forecasting. These_x000D_
models have been meticulously described and their properties have been rigorously proven. However, these_x000D_
same models also require a large number of assumptions, which may not necessarily hold in reality. One such_x000D_
assumption is that the dependence between events occurring further apart in time also tends to be smaller. We'd_x000D_
like to explore a data-driven model that aims to relax these assumptions. The recent explosion in accessibility_x000D_
to computational power has re-ignited interest in the Artificial Neural Net, which has shown success in many_x000D_
different areas of application. A modification to this is the Recurrent Neural Net. With the addition of an_x000D_
internal state variable, this neural-network set-up can be applied to sequential data as well. It had also been_x000D_
shown to be Turing complete, meaning that it can emulate any computer program given a large enough set of_x000D_
parameters. Applications of the RNN in natural language processing have been rather successful,_x000D_
demonstrating that this model is much better than classical statistical models in capturing complex sequential_x000D_
dependence, since the classical models require short-range dependence assumptions to work., agreement title english : Joint Prediction of Multiple Waiting Times with Recurrent Neural Nets, agreement title french : Joint Prediction of Multiple Waiting Times with Recurrent Neural Nets, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, 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