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In this paper, we explore the use of machine learning and data mining to improve the prediction of travel times in an automobile. We consider two formulations of this problem, one that involves predicting speeds at diierent stages along the route and another that relies on direct prediction of transit time. We focus on the second formulation, which we apply(More)
In this paper we propose a framework for estimation and quality control of conditional neural network volatility models for market risk management. In a first step, we derive a conditional volatility model based on gaussian mixture densities, that can be used with linear or neural regression models (extendable even to rule systems or decision trees). In a(More)
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