Editor: Cagdas H. Aladag

Advances in Time Series Forecasting

Volume 1

eBook: US $49 Special Offer (PDF + Printed Copy): US $113
Printed Copy: US $89
Library License: US $196
ISSN: 2543-2796 (Print)
ISSN: 2543-280X (Online)
ISBN: 978-1-60805-522-7 (Print)
ISBN: 978-1-60805-373-5 (Online)
Year of Publication: 2012
DOI: 10.2174/97816080537351120101


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.


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