25,051 research outputs found

    Reliability estimation of regressional predictions on data streams

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    With today's technology it is easy to collect data continuously. Still, how to extract knowledge from potentially infinite data streams remains an open problem. Because of specific constraints, stream processing methods have to be well designed, space-efficient, computationally simple and fast. Typically, data analysis is done on a fixed history of the data stream defined by a sliding window. We usually define the quality of predictions by their average accuracy. However, when dealing with real-time data it can be also important to know the reliability of the models’ output values. In this thesis we deal with online reliability estimation of individual predictions on data streams. We consider different interval reliability estimators based on maximum likelihood, bootstrap and local neighborhood approach for working on continuous dynamic data. We implement these methods on different regression models and test them on several real and artificial regression problems with various sizes of the sliding window. Performance of the interval estimates are evaluated using the estimates of prediction interval coverage probability, the relative mean prediction interval and the combined statistic. We compare the execution times of learning algorithms with and without the reliability estimates as well as their prediction accuracy when given the same time constraint. We also analyze results visually

    National and international freight transport models: overview and ideas for further development

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    This paper contains a review of the literature on freight transport models, focussing on the types of models that have been developed since the nineties for forecasting, policy simulation and project evaluation at the national and international level. Models for production, attraction, distribution, modal split and assignment are discussed in the paper. Furthermore, the paper also includes a number of ideas for future development, especially for the regional and urban components within national freight transport models

    Biogeography of Endemic Dragonflies of the Ozark-Ouachita Interior Highlands

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    A common pattern across many taxonomic groups is that relatively few species are widespread while the majority are restricted in their geographic ranges. Such species distributions are used to inform conservation status, which poses unique challenges for rare or cryptic species. Further, priority status is often designated within geopolitical boundaries, which may include only a portion of a species range. This, coupled with lack of distributional data, has resulted in species being designated as apparently rare throughout some portions of their range, which may not accurately reflect their overall conservation need. The Interior Highlands region of the central United States harbors a rich diversity of flora and fauna, many of which are regional endemics. Among these are four dragonfly species considered Species of Greatest Conservation Need: Ouachita spiketail (Cordulegaster talaria), Ozark Emerald (Somatochlora ozarkensis), Westfall’s snaketail (Ophiogomphus westfalli), and Ozark clubtail (Gomphurus ozarkensis). I combined species distribution modeling with field surveys to better understand the current biogeography for the two species with ample presence data (S. ozarkensis and G. ozarkensis). Additionally, models were used to project species’ distributions under two climate change scenarios of differing severity. To assess reliability of model predictions, I used two machine learning algorithms commonly used with limited, presence-only data. Current areas of suitability predicted by both algorithms largely overlapped for each species. An analysis of variable contribution showed congruence in important environmental predictors between models. Field validation of these models resulted in new detections for both species showing their utility in guiding future surveys. Future projections across two climate change scenarios showed the importance of maintaining current suitable areas as these will continue to be strongholds for these species under climate change
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