Advances in Time Series Forecasting

Volume: 1

Indexed in: Scopus, EBSCO, Ulrich's Periodicals Directory

Time series analysis is applicable in a variety of disciplines, such as business administration, economics, public finance, engineering, statistics, econometrics, mathematics and actuarial sciences. ...
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Advanced Time Series Forecasting Methods

Pp. 3-10 (8)

Cagdas Hakan Aladag and Erol Eǧrioǧlu

Abstract

The researchers from various fields have been studying on time series forecasting for approximately one century in order to get better forecasts for the future. To achieve high forecast accuracy level, various time series forecasting approaches have been improved in the literature. During 1980s, some crucial developments happened and time series researches changed. More sophisticated algorithms could be improved since properties of computers were enhanced. Therefore, new time series forecasting approaches such as artificial neural networks and fuzzy time series could be proposed. In the applications, these approaches have proved its success in forecasting real life time series. In addition, hybrid forecasting methods which combine these new approaches have also been improved to obtain more accurate forecasts. In recent years, these advanced time series forecasting methods have been used to forecast real life time series and satisfactory results have also been obtained.

Keywords:

Artificial neural networks, Fuzzy time series, Forecasting, Hybrid methods.

Affiliation:

Hacettepe University, Faculty of Science, Department of Statistics, 06800, Ankara, Turkey