Forecasting TAIEX using improved type 2 fuzzy time series

被引：38

作者：

Bajestani, Narges Shafaei ^{[1
]}

Zare, Assef ^{[1
]}

机构：

[1] Islamic Azad Univ, Gonabad Branch, Tehran, Iran

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 05期

关键词：

Forecasting; Fuzzy sets; High-order fuzzy time series; Linguistic modeling; ARIMA MODEL; ENROLLMENTS; PREDICTION;

D O I：

10.1016/j.eswa.2010.10.049

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

This paper presents a new method to forecast TAIEX based on a high-order type 2 fuzzy time series. Extra observations are used to improve forecasting performance. Extra observations are modeled as type 2 fuzzy sets and fourth-order fuzzy time series. Our proposed model outperforms previous studies. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：5816 / 5821

页数：6

共 27 条

[1]

[Anonymous], 1992, Fuzzy Regression Analysis

[2]

BAJESTANI NS, 2009, 2 INT C COMP CONTR C, P1

[3]

Forecasting enrollments based on fuzzy time series [J].

Chen, SM .

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

[4]

Temperature prediction using fuzzy time series [J].

Chen, SM ;

Hwang, JR .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2000, 30 (02) :263-275

[5]

CHENG CH, 2009, EXPERT SYSTEMS APPL, V2, P1126

[6]

CHOU MT, 2008, 4 INT C NETW COMP AD

[7]

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

[8]

A Type 2 fuzzy time series model for stock index forecasting [J].

Huarng, K ;

Yu, HK .

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2005, 353 (1-4) :445-462

[9]

HUARNG K, 2003, INT J FUZZY SYSTEMS, V5

[10]

HUARNG K, 2001, FUZZY SETS SYSTEMS, V123, P137

← 1 2 3 →