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We investigate the problem of churn detection and prediction using sequential cellular network data. We introduce a cleaning and preprocessing of the dataset that makes it suitable for the analysis. We draw a comparison of the churn prediction results from the-state-of-the-art algorithms such as the Gradient Boosting Trees, Random Forests, basic Long Short-Term Memory (LSTM) and Support Vector Machines...
We study online compound decision problems in the context of sequential prediction of real valued sequences. In particular, we consider finite state (FS) predictors that are constructed based on the sequence history. To mitigate overtraining problems, we define hierarchical equivalence classes and apply the exponentiated gradient (EG) algorithm to achieve the performance of the best state assignment...
We study online compound decision problems in the context of sequential prediction of real valued sequences. In particular, we consider finite state (FS) predictors that are constructed based on the sequence history, whose length is quite large for applications involving big data. To mitigate over training problems, we define hierarchical equivalence classes and apply the exponentiated gradient (EG)...
We study sequential prediction of real-valued, arbitrary, and unknown sequences under the squared error loss as well as the best parametric predictor out of a large, continuous class of predictors. Inspired by recent results from computational learning theory, we refrain from any statistical assumptions and define the performance with respect to the class of general parametric predictors. In particular,...
We study sequential prediction of energy consumption of actual users under a generic loss/utility function. Particularly, we try to determine whether the energy usage of the consumer will increase or decrease in the future, which can be subsequently used to optimize energy consumption. To this end, we use the energy consumption history of the users and define finite state (FS) predictors according...
This paper explores an emerging method with deep roots in machine learning and game theory that has been applied to a number of signal processing applications. This competitive algorithm-based framework is particularly attractive for applications in which there is a large degree of uncertainty in the statistics and behavior of the signals of interest. Problems of prediction, equalization and adaptive...
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