TABLE OF CONTENTS
TITLE PAGE
TABLE OF CONTENTS
LIST OF ABBREVIATIONS
ABSTRACT
CHAPTER ONE: INTRODUCTION
1.1 Background Information
1.2 Aim and Objectives
1.3 Statement of the Problem
1.4 Methodology
1.5 Significant Contributions
1.6 Thesis Organization
CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction
2.2 Review of Fundamental Concepts
2.2.1 Time Series
2.2.2 Fuzzy Set Theory
2.2.3 Fuzzy Time Series and Fuzzy Logic Relationship
2.2.4 Universe of Discourse
2.2.5 Fuzzy Set Groups
2.2.6 Data Mining and Clustering
2.2.6.1Distance Measure
2.2.7Fuzzy C-Meeans Clustering
2.2.8Cluster Validity Index
2.2.9 Particle Swarm Optimization
2.2.10 Defuzzification Operator
2.2.11Erlang Based Voice Traffic
2.2.12 Performance Measure
2.2.13 Programming Language
2.2.13.1C programming Language
2.2.13.2 C++ Programming Language
2.2.13.3 Java Programming Language
2.2.13.4 C# Programming Language
2.3 Review of Similar Works
CHAPTER THREE: MATERIAL AND METHODS
3.1 Introduction
3.2 Data Collection and Processing
3.3 Fuzzification Module
3.3.1 Coding fuzzy C-Means (FCM) Clustering Algorithm in C#
3.3.2 Applying Time Series Data on Fuzzy C-Means Code
3.3.3 Ranking Clusters in Ascending Order
3.3.4 Fuzzifying Time Series Data
3.4 Defuzzification Module
3.4.1 Establishing Fuzzy Set Groups (FSGs)
3.4.2 Converting Fuzzy Set Groups into “if – then” Rules
3.4.3 Tuning “if – then” Rules Using Particle Swarm Optimization (PSO)
3.4.4 Deriving Forecasts
3.5 Investigating the Effect of Reversed Weights
3.6 Forecasting Test Data Set
3.7 Forecasting Using Chen’s (1996) Fuzzy Time Series Model
3.8 Forecasting Using Cheng et al (2008) Hybrid Model
CHAPTER FOUR: RESULTS AND DISCUSSIONS
4.1 Introduction
4.2 Forecasting Results for Training Data Set
4.3 Forecasting Result for Test Data Set Forecasts
4.4 Validation
4.5 Significance of Forecasting Results
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
5.1 Summary
5.2 Conclusion
5.3 Limitations
5.4 Recommendations for Further Works
REFERENCES
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 1 December, 2012 – 28 February, 2013 from Airtel, Abuja region, was used to evaluate the proposed hybrid model.To evaluate the forecasting efficiency of the proposed hybrid model, its statistical performance measures of mean square error (MSE) and mean absolute percentage error (MAPE), were calculated and compared with those of a conventional fuzzy time series (FTS) model and, a fuzzy C-means (FCM) clustering and fuzzy time series (FTS) hybrid model.Statistical results of
MSE 0.9867 and MAPE 0.47 % were
|
obtained during training of the proposed hybrid
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forecasting model. Compared with the training results of MSE
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845.122 and MAPE 13.47 % ,
| |
for Chen‟s (1996) FTS model and; MSE
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856.145 and MAPE
|
13.37 % , for Cheng‟s (2008);
|
the proposed hybrid forecasting model resulted in a relatively higherforecasting accuracy and
precision. Also, performancemeasures of
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MSE
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59.22 and MAPE
|
3.85 % were
|
obtained
|
during thetesting phase of the proposed model. Compared with the test results of MSE
|
1567.4
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and MAPE 23.98 % obtained for Cheng‟s
|
(2008)
|
FCM/ FTS hybrid
|
model, the
|
proposed
|
hybrid forecasting model also showed a relatively higher forecasting accuracy and precision. Finally, it was determined that reversing the weights of the forecasting rules, during training,
resulted to a lesser performance; MSE
|
42.73 and MAPE
|
0.88 %. Thus, reversing the weights
| ||||
of
|
forecasting
|
rule
|
affected
|
the
|
forecasting
|
accuracy.
|
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND INFORMATION
Since its inception over three decades ago, mobile telecommunication call centres have witnessed exponential growth. Call centres are on the increase owing to the large number of mobile subscribers and the need for telecommunication operators to lower cost of providing services while increasing time access of their services. Understanding voice traffic pattern of a call centre becomes critical to service providers in predicting traffic, planning and budgeting for future changes of their mobile networks. This is important for sustaining a good Quality of Service (QoS).
Forecasting is used to predict, model and simulate the future from past events in virtually all fields of endeavours. In the telecommunication industry, forecasting is a useful tool in planning, budgeting, evaluating and verifying network resources (Eleruja et al, 2012).
Voice traffic is one of the critical measures in mobile telecommunication systems. Since this measure is non – linear and dynamic with time, forecasting Erlang based voice traffic observations using fuzzy time series (FTS) models seems to be more suitable than conventional statistical models. Fuzzy time series (FTS) models take care of uncertainties in observations over time and does not require any restrictive assumptions and too much background knowledge of the data; like in the case of conventional statistical forecasting methods.The use of fuzzy time series (FTS) in forecasting was first introduced by Song and Chissom (1993). This approach comprises two phases; fuzzification and defuzzification. Fuzzification is a technique for conversion of real observations into discrete or linguistic fuzzy sets. Defuzzification is a technique for converting linguistic observations to real values....
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