Researchers have been challenged to combine time series forecasting models, with the intention of enhancing forecast accuracy and efficiency. In this way, to weight models accuracy, efficiency, and mutual dependency becomes paramount. A promising way to address this issue is via copulas. Copulas are joint probability distribution functions aimed to envelop both the marginal distribution as well as the dependency among variables (e.g. forecasting models). This paper introduces copulas in the problem of combining time series forecasting models and proposes a maximum likelihood-based methodology in this context. Specifically, a Gumbel-Hougaard copulas model is presented. The usefulness of the resulting methodology is illustrated by means of simulated cases involving the combination of two single ARIMA models.
DESIGN OF SOFTWARE PRODUCTS AIMED AT INNOVATION: A CASE STUDY ABSTRACTThis paper presents results of a study case in the urban mobility area where was proposed a new software product, the ideation and conception was based on innovation-driven techniques such as: Long Tail, Free and with the Blue Ocean Strategy. Basically, the problem is related on the fact that number of vehicles in urban centers, resulting in a demand for more parking and thus intelligent system that can be integrated with parking and information in real location. A research conducted in large urban center found that 80,6% of people are interest in using this type of service, 100% never used any type of service this nature and 72,2% answered that would be willing to pay for to use this service. The results show the conception of a software product that search a Market niche little explored. This paper evaluates level of innovation via strategy canvas and blue ocean matrix, proposal by the blue ocean strategy. The result obtained by the software proposal was different relative to competitors, characterizing a value innovation and market potential.
Courses timetabling has been one of the main problems for planning, maintaining and optimizing educational institutions. However, the intriguing mathematical problem which usually result from the attempt of promoting optimal courses timetabling has prevented a widely dedication of education managers to this area. The present paper aims to summarize the usefulness of approximate techniques (e.g. genetic algorithms) for dealing with courses timetabling. In particular, the successful application of the resulting algorithm in a Brazilian university center is highlighted.
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