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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New Criteria to Compare Interval Estimates in Fuzzy Time Series Methods

Pp. 56-63 (8)

Erol Eǧrioǧlu, V. Rezan Uslu and Senem Koc


The idea of exploring fuzzy set theory to time series forecasting issues has been enormously attracted researcher’s attention in recent years. Several new approaches on fuzzy time series have been put forward. These approaches have got some advantages related to classical methods and are complementary of them. Two of these kinds of procedures are FARIMA and FSARIMA. FARIMA and FSARIMA do not require a restriction of at least 50 observations and linearity assumption. The methods of FARIMA and FSARIMA provide interval estimates of a time series. ARIMA and SARIMA also provide interval estimation but it has been put forward that estimated intervals are large, therefore not informative. The width of estimated intervals obtained from FARIMA and SARIMA may generally tend to be less than ones from ARIMA and SARIMA. In the literature, there has been no study which provides a criterion for the comparisons of time series with respect to interval estimates. In this study, two criteria for such comparisons are presented.


ARIMA, Fuzzy ARIMA, Fuzzy SARIMA, Interval estimates, SARIMA.


Ondokuz Mayis University, Faculty of Arts and Science, Department of Statistics, 55139, Samsun, Turkey