Assumption University
Bangkok, Thailand
December 1997
GENETIC
FORECASTING ALGORITHM
by
Somsong Chiraphadkanakul
A doctoral dissertation submitted in partial
fulfillment of the requirements for the degree of Doctor of Philosophy
in Computer Information Systems.
- Examination Committee :
- Prof. Dr. Srisakdi Charmonman
- Assoc.Prof. Somchai Tayarnyong
- Air Marshal Dr.Chulit Meesajjee
- Dr.Prapon Phasukyud
- Dr.Vichit Avatchanakorn
- Assist. Prof. Dr.Kamales Santivejkul
- Dr.Ouen Pin-ngen
- Dr.Aran Namphol
- Name :
- Somsong Chiraphadkanakul
- Nationality :
- Thai
- Previous Degree :
- Master of Sciences in CIS
- Assumption University, Thailand
- Master of Accountancy (Finance)
- Chulalongkorn University, Thailand
- Bachelor of Business Administration (Finance)
- Bangkok University, Thailand
Approval Page
RESEARCH TITLE : GENETIC Forecasting Algorithm
CANDIDATE NAME : Somsong Chiraphadkanakul
ADVISOR NAME : Dr.Vichai Avatchanakorn
ACADEMIC YEAR : 1997
The Graduate School of Assumption University had
approved this dissertation as a partial fulfillment of the requirement
for the degree of Doctor of Philosophy in Computer Information Systems
- Abstract
- This dissertation develops a forecasting algorithm
using the search and optimization features of genetic algorithms (GAs).
The proposed genetic forecasting algorithm combines the powerful search
of GAs and their capability to learn about pattern-relationships of
past data in order to forecast future values. The genetic forecasting
algorithm consists of two steps: the forecasting and the learning
steps. GAs are used in the forecasting step to estimate parameters
of the problem domain. The pattern learning is taken into account
in the algorithm to capture the pattern relationship of learning data,
The fitness function of the selection mechanism is defined by minimizing
the fitness function in which the errors from both the genetic forecasting
and the pattern learning steps are taken into account.
- The effectiveness of the proposed genetic forecasting
algorithm is examined by application to two applications domains :
commercial banks deposit forecasting and bankruptcy prediction. Results
of computer simulation show the accuracy in forecasting commercial
banks deposit model. In addition, the proposed algorithm has better
forecasting performance as compared to traditional forecasting methods.
In bamkruptcy prediction model, the results of computer simulation
show tlhe predictive ability of the proposed algorithm which has better
classification ability than the traditional methods for classifying
bankurpt and nonbankrupt firms.
- Acknowledgements
- The author is grateful to the numerous individuals
who contributed to the preparation of this dissertation. First, the
author wishes to thank Dr. Vichit Avatchanakorn the advisor of this
research, for this continuing advice, support and correction throughout
this research work.
- Next, the author wishes to thank Prof. Dr.
Srisakdi Charmonman for this support and advice throughout the doctoral
program,
- The author wishes to thank the qualifying examination
committee : Assoc. Prof. Somchai Tayarnyong, Air Marshal Dr. Chulit
Meesajjee, and Dr.Prapon Phasukyud, for their valuable guidance on
the study in this dissertation, and her thanks to Dr. Aran Namphol,
Dr. Ouen Pin-ngen for their valuable time in going through this dissertation.
- The author wishes to thank Dr. Pataya Dangprasert
for her continuing suggestions and recommendations. Finally, the author
wishes to thank Assist, Prof. Dr. Kamales Santivejkul for his support
in financial data and continuing encouragement.
- It hardly needs saying that much of the value of
this dissertation is due to their assistance, but the author alone
bears responsibility for any errors or omissions that remain between
the covers.
- Table of Contents
- List of Figures
- list of Table
- Nomenclatures
- Chapter 1 : Introduction
-
- 1.1 Introduction
- 1.2 Problem Definition
- 1.3 Literature Review
- 1.4 Objective of Study
- 1.5 Scope of Study
- Chapter 2 : Genetic Forecasting Algorithm
-
- 2.1 Introduction
- 2.2 Genetic Algorithms
- 2.3 Mathematical Representation of Problem Domain
- 2.4 Genetic Forecasting Algorithm
-
- 2.4.1 Genetic Forecasting Loop
- 2.4.2 Pattern Learning Loop
- 2.4.3 Selection Mechanism
- Chapter 3 : Genetic Forecasting of Commercail
Banks Deposit
-
- 3.1 Introduction
- 3.2 Problem Domain Model
-
- 3.2.1 Definition of Variables
- 3.3 Genetic Forecasting of Commercial Banks
Deposit
-
- 3.3.1 Genetic Forecasting Loop
- 3.3.2 Pattern Learning Loop
- 3.3.3 Selection Mechanism
- 3.4 Simulation Results
-
- 3.4.1 Sensitivity Analysis
- 3.4.2 Forecasting Results
- 3.5 Conclusions
- Chapter 4 : Genetic Bankruptcy Prediction
-
- 4.1 Introduction
- 4.2 Bankruptcy Model
-
- 4.2.1 Definion of Variables
- 4.2.2 Learning and Testing Data Sets
- 4.3 Genetic Bankruptcy Prediction
- 4.4 Results and Comparisons
-
- 4.4.1 Bankruptcy Prediction Results
- 4.4.2 Results Comparisons
- 4.5 Conclusions
- Chapter 5 : Conclusion and Recommendations
-
- 5.1 Conclusions
- 5.2 Recommendations
- Appendix A : Detailed Definition of Variables in
Commercial Banks Deposit Model
- Appendix B : Data of Variables in Commercial Banks
Deposit Model
- Appendix C : Year by Year Banks Deposit Forecasting
Results
- Appendix D : Artificial Neural Networks
- Appendix E : Multiple Regression Analysis
- References
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