ARMA model orders and parameters estimation using evolutionary algorithm
Other Title(s)
تقدير درجة و معاملات متسلسلات المتوسط المتحرك التدريجي الذاتي باستخدام الخوارزميات المتطورة
Dissertant
Thesis advisor
al-Hamami, Ala Husayn
al-Smadi, Adnan Muhammad Khayr
Comitee Members
Ubayd, Nadim Ali Miri
al-Ani, Muzhir Shaban
Qaṭawinah, Muhammad Sulayman
University
Amman Arab University
Faculty
Collage of Computer Sciences and Informatics
Department
Department of Computer Science
University Country
Jordan
Degree
Ph.D.
Degree Date
2010
English Abstract
Autoregressive Moving Average (ARMA) model is an active research area that has many major roles in a variety of application fields.
For examples, ARMA process plays an important role in modeling the Internet traffic of a network, financial market forecasting, biomedical signals analysis, speech modeling, radar, sonar, linear prediction, system identification, and spectral analysis.
Model order selection of a general ARMA process has been of considerable interest for some time and it has a long and continuity history.
ARMA model is one of the most important time series models.
An important step in system identification and time series prediction is the estimation of the model order.
The fundamental problem of the ARMA modeling is that, the model order is not known and it needs to be estimated before solving the parameters estimation problem.
Hence, determining the order of the ARMA model is the first step to estimate the model parameters.
Selecting the optimal ARMA model order is difficult and has never been obtained satisfactorily.
This dissertation provides two novel algorithms for solving the problem of ARMA model order estimation.
The two proposed algorithms are based on the algorithm proposed by Liang et al.
The observed sequence may be contaminated by additive Gaussian noise.
Simulation examples are given to demonstrate the performance of the proposed algorithms at different Signal-to-Noise-Ratios (SNRs).
The computations were performed in MATLAB.
The first proposed algorithm, called Pivot-Neighbors comparisons (PNC), is based on a rounding approach which uses the floor and the ceiling functions.
The rounding approach is implemented to deal with the precision of binary words.
The proposed algorithm is based on selecting a sequence of pivot cells from the Minimum Eigenvalue (MEV) of a covariance matrix computed from the observed data.
It searches for a corner that contains the estimates of the true orders using the floor and the ceiling functions of the pivot cells values and the values of its neighbors.
The second proposed algorithm is based on modeling and designing Artificial Neural Network (ANN) architecture for a special matrix constructed from the MEV criterion to estimate the ARMA model orders.
The proposed ANN-based algorithm is based on training the MEV dataset using the Back-Propagation (BP) learning algorithm.
Our goal is to develop an automated system; hence, the model order can be selected automatically without the need of prior knowledge about the model or any human expert participation.
The empirical results of several ARMA model examples indicate that the proposed algorithms have improved estimation accuracy over the MEV method
Main Subjects
No. of Pages
192
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Theoretical background.
Chapter Three : ARMA model order estimation via pivot-neighbors comparisons.
Chapter Four : ARMA model orders estimation using artificial neural networks.
Chapter Five : ANN outputs analysis.
Chapter Six : Conclusions and future work.
References.
American Psychological Association (APA)
al-Qawasimi, Khalid Isa. (2010). ARMA model orders and parameters estimation using evolutionary algorithm. (Doctoral dissertations Theses and Dissertations Master). Amman Arab University, Jordan
https://search.emarefa.net/detail/BIM-528454
Modern Language Association (MLA)
al-Qawasimi, Khalid Isa. ARMA model orders and parameters estimation using evolutionary algorithm. (Doctoral dissertations Theses and Dissertations Master). Amman Arab University. (2010).
https://search.emarefa.net/detail/BIM-528454
American Medical Association (AMA)
al-Qawasimi, Khalid Isa. (2010). ARMA model orders and parameters estimation using evolutionary algorithm. (Doctoral dissertations Theses and Dissertations Master). Amman Arab University, Jordan
https://search.emarefa.net/detail/BIM-528454
Language
English
Data Type
Arab Theses
Record ID
BIM-528454
