A New Approach Based on Artificial Neural Networks for High Order Bivariate Fuzzy Time Series

被引：21

作者：

Egrioglu, Erol ^{[1
]}

Uslu, V. Rezan ^{[1
]}

Yolcu, Ufuk ^{[1
]}

Basaran, M. A. ^{[2
]}

Hakan, Aladag C. ^{[3
]}

机构：

[1] Ondokuz Mayis Univ, Dept Stat, TR-55139 Samsun, Turkey

[2] Nigde Univ, Dept Math, TR-51000 Nigde, Turkey

[3] Hacettepe Univ, Dept Stat, TR-06800 Ankara, Turkey

来源：

APPLICATIONS OF SOFT COMPUTING: FROM THEORY TO PRAXIS | 2009年 / 58卷

关键词：

FORECASTING ENROLLMENTS; MODELS;

D O I：

10.1007/978-3-540-89619-7_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

When observations of time series are defined linguistically or do not follow the assumptions required for time series theory, the classical methods of time series analysis do not cope with fuzzy numbers and assumption violations. Therefore, forecasts are not reliable. [8], [9] gave a definition of fuzzy time series which have fuzzy observations and proposed a forecast method for it. In recent years, many researches about univariate fuzzy time series have been conducted. In [6], [5], [7], [4] and [10] bivariate fuzzy time series approaches have been proposed. In this study, a new method for high order bivariate fuzzy time series in which fuzzy relationships are determined by artificial neural networks (ANN) is proposed and the real data application of the proposed method is presented.

引用

页码：265 / +

页数：3

共 13 条

[1] Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations [J].

Aladag, Cagdas H. ;

Basaran, Murat A. ;

Egrioglu, Erol ;

Yolcu, Ufuk ;

Uslu, Vedide R. .

EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) :4228-4231

[2] Forecasting enrollments based on fuzzy time series [J].

Chen, SM .

FUZZY SETS AND SYSTEMS, 1996, 81 (03) :311-319

[3] Forecasting enrollments based on high-order fuzzy time series [J].

Chen, SM .

CYBERNETICS AND SYSTEMS, 2002, 33 (01) :1-16

[4]

CHENG CH, 2007, EXPERT SYSTEMS APPL, DOI DOI 10.1016/J.ESWA.2007.09.037

[5] A new approach of bivariate fuzzy time series analysis to the forecasting of a stock index [J].

Hsu, YY ;

Tse, SM ;

Wu, B .

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2003, 11 (06) :671-690

[6] Heuristic models of fuzzy time series for forecasting [J].

Huarng, K .

FUZZY SETS AND SYSTEMS, 2001, 123 (03) :369-386

[7] Handling forecasting problems based on two-factors high-order fuzzy time series [J].

Lee, Li-Wei ;

Wang, Li-Hui ;

Chen, Shyi-Ming ;

Leu, Yung-Ho .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2006, 14 (03) :468-477

[8] FORECASTING ENROLLMENTS WITH FUZZY TIME-SERIES .1. [J].

SONG, Q ;

CHISSOM, BS .

FUZZY SETS AND SYSTEMS, 1993, 54 (01) :1-9

[9] FUZZY TIME-SERIES AND ITS MODELS [J].

SONG, Q ;

CHISSOM, BS .

FUZZY SETS AND SYSTEMS, 1993, 54 (03) :269-277

[10] A bivariate fuzzy time series model to forecast the TAIEX [J].

Yu, Tiffany Hui-Kuang ;

Huarng, Kun-Huang .

EXPERT SYSTEMS WITH APPLICATIONS, 2008, 34 (04) :2945-2952

← 1 2 →