The goal of this paper is to present a quantitative study about Telco churn analysis and the inherent problems of its application and computer processing.An independent customer characterizes the approach and the pre-processing step uses the wavelet transform concept; the pattern classification problem is solved by an artificial neural network (RBF-Radial Basis Function).The signal representation considers different levels of resolution (multi-resolution). This idea is frequently used in image/video processing where general image versions with a low resolution are stored in different levels of resolution. The concepts of multi-resolution have been implemented by a digital filter bank, Hi-pass and Low-pass. Each sub-band will give the best resolution, for each spectral region. The idea is to look for a spectral transient.A scalable Daubechies' function db 2 and db 3 were tested for this study and the results were used as input for the RBF neural network. Daubechies' function has shown important properties as for instance, orthogonality and scalability, which are not presented by Morlet's function.Some results of the experiment were presented to point out the performance of the wavelet pre-processing approach.
The objective of this paper is to present a database marketing analysis through data and text mining tools. A case study of a Brazilian Power Energy distribution was developed indoors. The main idea is to transform the database information into strategic marketing knowledge. Thus a data warehouse sample was treated, reduced and clustered. Principal component analysis was used to reduce the original number of variables. The entire database was classified after creation by the decision trees and neural networks approach. In this work, text mining techniques were used to process customers' claims in order to improve cluster results. The CRM group has developed a powerful tool to gather knowledge regarding the skills and habits of customers, thereby gaining their confidence and loyalty.
Load forecasting is an important subject for power distribution systems and has been studied comparing different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short-term, medium-term and long-term forecasts. Several research groups have proposed various techniques for either short-term load forecasting or medium-term load forecasting or long-term load forecasting. This paper presents two approaches for modelling the long-term load forecasting: a neural network (NN) and a non-linear (cause/effect) model. The data used by the models are gross domestic product (GDP), the national minimum salary, the electrical energy price, the estimated national population and the total number of electrical connections.The suitability of the proposed approach is illustrated through a long-term load forecasting application (electricity consumption in Brazil ten years ahead).
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