Editor: "Cagdas H. Aladag"

Advances in Time Series Forecasting

Volume 1

Personal Book: US $49 Special Offer (PDF + Printed Copy): US $113
Printed Copy: US $89
Library Book: US $196
ISSN: 2543-2796
eISSN: 2543-280X (Online)
ISBN: 978-1-60805-522-7
eISBN: 978-1-60805-373-5 (Online)
DOI: 10.2174/97816080537351120101

Introduction

Time series analysis is applicable in a variety of disciplines, such as business administration, economics, public finance, engineering, statistics, econometrics, mathematics and actuarial sciences. Forecasting the future assists in critical organizational planning activities. Time series analysis is employed by many different organizations such as hospitals, universities, commercial enterprises or government organizations in order to forecast future scenarios. Therefore, many time series forecasting methods have been proposed and improved in statistical literature. Linear models such as Box-Jenkins methods were earlier used in many situations. Then, to overcome the restrictions of these linear models and to account for certain nonlinear patterns observed in real problems, some nonlinear models are also presented in literature. However, since these nonlinear models were developed for specific nonlinear patterns, they are not suitable for modeling other types of nonlinearity in time series. In recent years, efficient and advanced 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 analyses. Both theoretical and empirical findings in academic literature show that these approaches give comparatively reliable forecasts than those obtained from conventional forecasting methods. In addition, conventional models require some assumptions such as linearity and normal distribution or cannot be utilized efficiently for some real time series such as temperature and share prices of stockholders since these kind of series contain some uncertainty. However, when advanced 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 containing uncertainty can be forecasted efficiently

This e-book contains recent effective applications and descriptions of these advanced forecasting methods. Readers will 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 combines different methods to obtain better forecast results, is also explained. Readers can also find the applications of hybrid forecasting models in this e-book. This e-book also enables skilled statisticians to create a new hybrid forecasting model suitable for their own objectives. Data presented in this e-book is problem based and is taken from real life situations. This e-book is a valuable resource for students, statisticians and working professionals interested in advanced time series analysis.

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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