Improving a fuzzy neural network for predicting storage usage and calculating customer value
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RESEARCH ARTICLE
Improving a fuzzy neural network for predicting storage usage and calculating customer value Reza Rabieyan1 · Philipp Pohl1 Received: 8 March 2020 / Accepted: 22 May 2020 / Published online: 2 July 2020 © The Author(s) 2020
Abstract Predicting the behavior of customers plays a crucial role in the quality of resource management and customer services. In this article, a fuzzy neural network model for predicting the customer storage usage is identified. The identified fuzzy neural network is improved and finally the result of the improved fuzzy neural network is compared with some other fuzzy neural network and other prediction methods. Keywords Machine learning · Fuzzy neural networks · Resource management · Storage usage · Adaptive second-order algorithm
Introduction
Philipp Pohl philipp.pohl@dhbw‑karlsruhe.de
maintenance management. Another great advantage of this prediction methodology is that the web hosting company can predict the achievable future cash flows of customers and it helps them to determine and analyze customer’s portfolio, by calculating customer value. Customer value is defined as sum of customers’ discounted future cash flows. The first step for predicting the storage usage of customers applies Poisson distribution theory which approximate to the Markov process. For instance, the forecasting model based on the autoregressive method is proposed by Barba and Rodríguez (2015). In reference to the autocorrelation properties of customer storage usage, applying the Poisson process is not a reliable method (Iliev and Bedzhev 2015; Morikawa and Tsuneda 2014). The linear model such as the Markov-modulated Poisson process and the moving average model are applied efficiently for short-term forecasting (Mai et al. 2014; Borchers and Langrock 2015) but by increasing the forecasting step the forecasting error will increase slowly (Hou et al. 2018). In fact, because of the strong non-linear behavior of web hosting customers, the customer storage usage model is a non-linear system and applying the classical prediction methods is not a reliable strategy for our purpose. One of the most popular tools for predicting the nonlinear and time varied behavior is the artificial neural network. The artificial neural network has some processing functions such as learning, memorizing, and computing,
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According to the report of the 12th annual Cisco Visual Networking Index Complete Forecast,1 the number of internet users will increase from 3.3 billion to 4.6 billion by 2021. From now to 2021 the improvement equates to 61% of the global population using the internet. Easy access has led to an ever-increasing use of the internet. Today, more and more information is uploaded from websites, and users download this information. Therefore, web hosting customers request more from web host providers. The inevitable result is that the controllability and manageability of the web host resources will be enhanced. Web hosting companies provide service for individuals and organizations to create their websites.
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