• A grouping genetic algorithm for joint stratification and sample allocation designs 

  O’Luing, Mervyn; Prestwich, Steven D.; Tarim, S. Armagan (Statistics Canada, 2019-12-17)

  Finding the optimal stratification and sample size in univariate and multivariate sample design is hard when the population frame is large. There are alternative ways of modelling and solving this problem, and one of the ...
• Intermittency and obsolescence: A Croston method with linear decay 

  Prestwich, Steven D.; Tarim, S. Armagan; Rossi, Roberto (Elsevier, 2020-11-03)

  Only two forecasting methods have been designed specifically for intermittent demand with possible demand obsolescence: Teunter–Syntetos–Babai (TSB) and Hyperbolic-Exponential Smoothing (HES). When an item becomes obsolete ...
• Computing optimal (R,s,S) policy parameters by a hybrid of branch-and-bound and stochastic dynamic programming 

  Visentin, Andrea; Prestwich, Steven D.; Rossi, Roberto; Tarim, S. Armagan (Elsevier, 2021-01-13)

  A well-known control policy in stochastic inventory control is the policy, in which inventory is raised to an order-up-to-level S at a review instant R whenever it falls below reorder-level s. To date, little or no work ...
• Machine learning for financial applications: self-organising maps, hierarchical clustering and dynamic time-warping for portfolio constructive 

  Emerson, Sophie (University College Cork, 2019-12-15)

  This study investigates how modern machine learning (ML) techniques can be used to advance the field of quantitative investing. A broad literature review evaluated the common applications for ML in finance, and what ML ...
• Applications of machine learning in finance: analysis of international portfolio flows using regime-switching models 

  Ó Cinnéide, Ruairí (University College Cork, 2019)

  Recent advances in machine learning are finding commercial applications across many sectors, not least the financial industry. This thesis explores applications of machine learning in quantitative finance through two ...

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