Advances in Time Series Forecasting


by

Cagdas H. Aladag

DOI: 10.2174/97816080537351120101
eISBN: 978-1-60805-373-5, 2012
ISBN: 978-1-60805-522-7



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Time series analysis is applicable in a variety of disciplines, such as business administration, economics, public finance, engineerin...[view complete introduction]

Table of Contents

Foreword

- Pp. i

I. Burhan Turksen

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Preface

- Pp. ii

Cagdas Hakan Aladag

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List of Contributors

- Pp. iii

.

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Advanced Time Series Forecasting Methods

- Pp. 3-10 (8)

Cagdas Hakan Aladag and Erol Eǧrioǧlu

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Comparison of Feed Forward and Elman Neural Networks Forecasting Ability: Case Study for IMKB

- Pp. 11-17 (7)

Erol Eǧrioǧlu, Cagdas Hakan Aladag and Ufuk Yolcu

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Comparison of Architecture Selection Criteria in Analyzing Long Memory Time Series

- Pp. 18-25 (8)

Erol Eǧrioǧlu, Cagdas Hakan Aladag and Ufuk Yolcu

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Forecasting the Number of Outpatient Visits with Different Activation Functions

- Pp. 26-33 (8)

Cagdas Hakan Aladag and Sibel Aladag

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Adaptive Weighted Information Criterion to Determine the Best Architecture

- Pp. 34-39 (6)

Cagdas Hakan Aladag and Erol Eǧrioǧlu

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Public Expenditure Forecast by Using Feed Forward Neural Networks

- Pp. 40-47 (8)

Alparslan A. Basaran, Cagdas Hakan Aladag, Necmiddin Bagdadioglu and Suleyman Gunay

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A New Method for Forecasting Fuzzy Time Series with Triangular Fuzzy Number Observations

- Pp. 48-55 (8)

Erol Eǧrioǧlu, Cagdas Hakan Aladag and Ufuk Yolcu

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New Criteria to Compare Interval Estimates in Fuzzy Time Series Methods

- Pp. 56-63 (8)

Erol Eǧrioǧlu, V. Rezan Uslu and Senem Koc

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The Effect of the Length of Interval in Fuzzy Time Series Models on Forecasting

- Pp. 64-77 (14)

Erol Eǧrioǧlu and Cagdas Hakan Aladag

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Determining Interval Length in Fuzzy Time Series by Using an Entropy Based Approach

- Pp. 78-87 (10)

Cagdas Hakan Aladag, Irem Degirmenci and Suleyman Gunay

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An Architecture Selection Method Based on Tabu Search

- Pp. 88-95 (8)

Cagdas Hakan Aladag

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A Hybrid Forecasting Approach Combines SARIMA and Fuzzy Time Seriesc

- Pp. 96-107 (12)

Erol Eǧrioǧlu, Cagdas Hakan Aladag and Ufuk Yolcu

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Forecasting Gold Prices Series in Turkey by the Forecast Combination

- Pp. 108-117 (10)

Cagdas Hakan Aladag, Erol Eǧrioǧlu and Cem Kadilar

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A Hybrid Forecasting Model Based on Multivariate Fuzzy Time Series and Artificial Neural Networks

- Pp. 118-129 (12)

Cagdas Hakan Aladag and Erol Eǧrioǧlu

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Subject Index

- Pp. 130-131 (2)

Cagdas Hakan Aladag and Erol Eǧrioǧlu

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Author Index

- Pp. 132-135 (4)

Cagdas Hakan Aladag and Erol Eǧrioǧlu

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Foreword

Generally, advanced intelligent techniques are needed to model and solve the problems encountered in various fields and also to reach desired answers. Many institutions have used various soft computing methods to solve the problems they faced, to increase their productivity and to make strategic decisions. Hence, these methods have received more attention in recent years. In turn, practitioners and academics from various fields have been working on these approaches

Time series forecasting is one of the most challenging contemporary tasks that are being faced in different areas. In general, different types of time series have been tried for the forecasting purpose. Unfortunately, conventional time series approaches for forecasting can be insufficient in modeling real life time series. Therefore, advanced methods such as artificial neural networks and fuzzy time series have been utilized in many applications. In this eBook, advanced forecasting approaches are described, and further explained how these approaches can be used to forecast real life time series. In particular, some new forecasting approaches are firstly introduced in this eBook. In addition, this eBook provides the background for describing new methods and improving existing advanced forecasting approaches. Dr. Cagdas Hakan Aladag and Assoc. Prof. Dr. Erol Egrioglu, the editors of this eBook, have made meaningful contributions to the literature regarding time series forecasting in the recent past. I believe, this eBook will be useful for both practitioners and researchers who are interested in receiving comprehensive views and insights from the variety of issues covered in this eBook

I. Burhan Turksen
TOBB Economy and Technology University
Turkey


Preface

Time series analysis has got attention of many researches from different fields, such as business administration, economics, public finances, engineering, statistics, econometrics, mathematics and actuarial sciences. When many organizations are planning their future, they have to forecast the future. Time series analysis has been employed by many organizations, such as hospitals, universities, companies or government organizations in order to forecast how could be the future. Therefore, many time series forecasting methods have been proposed and improved in the literature. Firstly, linear models such as Box-Jenkins methods were used in many areas of time series forecasting. Furthermore, to overcome the restriction of the linear models and to account for certain nonlinear patterns observed in real problems, some nonlinear models have been proposed in the literature. However, since these nonlinear models were developed for specific nonlinear patterns, they are not capable of modeling other types of nonlinearity in time series. In recent years, to overcome these issues, efficient soft computing techniques such as artificial neural networks, fuzzy time series and some hybrid models have been used to forecast any kind of real life time series. Both theoretical and empirical findings in the literature show that these approaches give better forecasts than those obtained from conventional forecasting methods. In addition, conventional models require some assumptions such as linearity and normal distribution cannot be utilized efficiently for some real time series such as temperature and share prices of stockholders, since this kind of series contain some uncertainty in itself. However, when soft computing methods such as neural networks and fuzzy time series are used to forecast time series, there is no need to satisfy any assumption and the time series uncertainty can be forecasted efficiently.

This eBook contains recent applications and descriptions of these effective soft computing methods. The readers can learn how these methods work and how these approaches can be used to forecast real life time series. In addition, the hybrid forecasting model approach, which is based on combining different soft computing methods to get better forecasts, is explained and at the same time, the reader can find the applications of hybrid forecasting models. The reader of this eBook can also create a new hybrid forecasting model. Although the soft computing forecasting models have many advantages, at the same time there are still some problems with their usage. These problems are pointed out in this ebook. After researchers see those problems, they make some contributions to these forecasting methods by filling some gaps to obtain better forecast results. Furthermore, some new forecasting models are introduced in the eBook.

Cagdas Hakan Aladag
Department of Statistics
Hacettepe University
Bolzano University
Turkey

List of Contributors

Editor(s):
Cagdas H. Aladag
Hacettepe University
Turkey




Co-Editor(s):
Erol Eğrioğlu
Ondokuz Mayis University
Turkey




Contributor(s):
C.H Aladag
Department of Statistics
Hacettepe University
Turkey


S Aladag
General Director of Service Provision
Republic of Turkey Social Security Institution
Ankara
Turkey


N Bagdadioglu
Department of Public Finance
Hacettepe University
Ankara
Turkey


A.A. Basaran
Department of Public Finance
Hacettepe University
Ankara
Turkey


I Degirmenci
Department of Statistics
Hacettepe University
Ankara
Turkey


E Eǧrioǧlu
Department of Statistics
Ondokuz Mayis University
Turkey


S Gunay
Department of Statistics
Hacettepe University
Ankara
Turkey


C Kadilar
Department of Statistics
Hacettepe University
Ankara
Turkey


S Koc
Department of Statistics
Ondokuz Mayis University
Samsun
Turkey


V.R. Uslu
Department of Statistics
Ondokuz Mayis University
Samsun
Turkey


U Yolcu
Department of Statistics
Giresun University
Giresun
Turkey




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