APPLICATION OF FUZZY C-MEANS CLUSTERING AND PARTICLE SWARM OPTIMIZATIONTO IMPROVE VOICE TRAFFIC FORECASTINGIN FUZZY TIME SERIES

📄 Project Abstract

Forecasting of voice traffic using an accurate model is important to the telecommunication service provider in planning a sustainable Quality of Service (QoS) for their mobile networks. This work is aimed at forecasting Erlang C - based voice traffic using a hybrid forecasting model that integrates fuzzy C-means clustering (FCM) and particle swarm optimization (PSO) algorithms with fuzzy time series (FTS) forecasting model. Fuzzy C-means (FCM) clustering, which is an algorithm for data classification, is adopted at the fuzzification phase to obtain unequal partitions. Particle swarm optimization (PSO), which is an evolutional search algorithm, is adopted to optimize the defuzzification phase; by tuning weights assigned to fuzzy sets in a rule.This rule is a fuzzy logical relationship induced from a fuzzy set group (FSG). The clustering and optimization algorithms were implemented in programs written in C#. Daily Erlang C traffic observations collected over a three (3) month period from...