12,770 research outputs found
Forecasting of commercial sales with large scale Gaussian Processes
This paper argues that there has not been enough discussion in the field of
applications of Gaussian Process for the fast moving consumer goods industry.
Yet, this technique can be important as it e.g., can provide automatic feature
relevance determination and the posterior mean can unlock insights on the data.
Significant challenges are the large size and high dimensionality of commercial
data at a point of sale. The study reviews approaches in the Gaussian Processes
modeling for large data sets, evaluates their performance on commercial sales
and shows value of this type of models as a decision-making tool for
management.Comment: 1o pages, 5 figure
Building accurate radio environment maps from multi-fidelity spectrum sensing data
In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated
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