A novel forecasting method based on multi-order fuzzy time series and technical analysis

•The directional accuracy rate of forecasting model is first selected as performance measures.•Exponential smoothing is used to eliminate noise in the time series.•Multi-order and multivariate fuzzy time series are combined to forecast financial time series.•The proposed method outperforms the exist...

Full description

Saved in:
Bibliographic Details
Published inInformation Sciences Vol. 367-368; pp. 41 - 57
Main Authors Ye, Furong, Zhang, Liming, Zhang, Defu, Fujita, Hamido, Gong, Zhiguo
Format Journal Article
LanguageEnglish
Japanese
Published Elsevier Inc 01.11.2016
Elsevier BV
Subjects
Online AccessGet full text

Cover

Loading…
Abstract •The directional accuracy rate of forecasting model is first selected as performance measures.•Exponential smoothing is used to eliminate noise in the time series.•Multi-order and multivariate fuzzy time series are combined to forecast financial time series.•The proposed method outperforms the existing methods for 5 well-known financial time series. Financial trading is one of the most common risk investment actions in the modern economic environment because financial market systems are complex non-linear dynamic systems. It is a challenge to develop the inherent rules using the traditional time series prediction technique. In this paper, we proposed a new forecasting method based on multi-order fuzzy time series, technical analysis, and a genetic algorithm. Multi-order fuzzy time series (first-order, second-order and third-order) are applied in the proposed algorithm, and to improve the performance, genetic algorithm is used to find a good domain partition. Technical analysis such as the Rate of Change (ROC), Moving Average Convergence/Divergence (MACD), and Stochastic Oscillator (KDJ) are introduced to construct multi-variable fuzzy time series, and exponential smoothing is used to eliminate noise in the time series. In addition to the root mean square error and mean square error, the directional accuracy rate (DAR) is also used in our empirical studies. We apply the proposed method to forecast five well-known stock indexes and the NTD/USD exchange rates. Experimental results demonstrate that our proposed method outperforms other existing models based on fuzzy time series.
AbstractList •The directional accuracy rate of forecasting model is first selected as performance measures.•Exponential smoothing is used to eliminate noise in the time series.•Multi-order and multivariate fuzzy time series are combined to forecast financial time series.•The proposed method outperforms the existing methods for 5 well-known financial time series. Financial trading is one of the most common risk investment actions in the modern economic environment because financial market systems are complex non-linear dynamic systems. It is a challenge to develop the inherent rules using the traditional time series prediction technique. In this paper, we proposed a new forecasting method based on multi-order fuzzy time series, technical analysis, and a genetic algorithm. Multi-order fuzzy time series (first-order, second-order and third-order) are applied in the proposed algorithm, and to improve the performance, genetic algorithm is used to find a good domain partition. Technical analysis such as the Rate of Change (ROC), Moving Average Convergence/Divergence (MACD), and Stochastic Oscillator (KDJ) are introduced to construct multi-variable fuzzy time series, and exponential smoothing is used to eliminate noise in the time series. In addition to the root mean square error and mean square error, the directional accuracy rate (DAR) is also used in our empirical studies. We apply the proposed method to forecast five well-known stock indexes and the NTD/USD exchange rates. Experimental results demonstrate that our proposed method outperforms other existing models based on fuzzy time series.
Financial trading is one of the most common risk investment actions in the modern economic environment because financial market systems are complex non-linear dynamic systems. It is a challenge to develop the inherent rules using the traditional time series prediction technique. In this paper, we proposed a new forecasting method based on multi-order fuzzy time series, technical analysis, and a genetic algorithm. Multi-order fuzzy time series (first-order, second-order and third-order) are applied in the proposed algorithm, and to improve the performance, genetic algorithm is used to find a good domain partition. Technical analysis such as the Rate of Change (ROC), Moving Average Convergence/Divergence (MACD), and Stochastic Oscillator (KDJ) are introduced to construct multi-variable fuzzy time series, and exponential smoothing is used to eliminate noise in the time series. In addition to the root mean square error and mean square error, the directional accuracy rate (DAR) is also used in our empirical studies. We apply the proposed method to forecast five well-known stock indexes and the NTD/USD exchange rates. Experimental results demonstrate that our proposed method outperforms other existing models based on fuzzy time series.
Author Ye, Furong
Zhang, Liming
Gong, Zhiguo
Fujita, Hamido
Zhang, Defu
Author_xml – sequence: 1
  givenname: Furong
  surname: Ye
  fullname: Ye, Furong
  organization: Department of Computer Science, Xiamen University, Xiamen, 361005, China
– sequence: 2
  givenname: Liming
  surname: Zhang
  fullname: Zhang, Liming
  organization: Department of Computer and Information Science, University of Macau, Macau, China
– sequence: 3
  givenname: Defu
  surname: Zhang
  fullname: Zhang, Defu
  email: dfzhang@xmu.edu.cn
  organization: Department of Computer Science, Xiamen University, Xiamen, 361005, China
– sequence: 4
  givenname: Hamido
  surname: Fujita
  fullname: Fujita, Hamido
  organization: Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan
– sequence: 5
  givenname: Zhiguo
  surname: Gong
  fullname: Gong, Zhiguo
  organization: Department of Computer and Information Science, University of Macau, Macau, China
BackLink https://cir.nii.ac.jp/crid/1870865117809378048$$DView record in CiNii
BookMark eNp9kTtrHDEUhUVwIGsnPyCdihRpZqzHSKMhlTF5gcGNi3RC0tyJ7zIjOZLWsP710bKpUri5DzjnHvjuJbmIKQIhHznrOeP6et9jLL1oY89Uz6R5Q3bcjKLTYuIXZMeYYB0TSr0jl6XsGWPDqPWO_LqhMT3DSpeUIbhSMf6mG9THNFPvCsw0Rbod1opdyjNkuhxeXo604ga0QEYo1MWZVgiPEYNb2-bWY8Hynrxd3Frgw79-RR6-fX24_dHd3X__eXtz14VBqNoJD9L74NykRqPlNASvzMwX8EoECcpJGeQipBiMZ6C912zR4xwc12FQg7win89nn3L6c4BS7YYlwLq6COlQLDdSaa4mMzXpeJaGnErJsNiA1VVMsWaHq-XMnlDavW0o7QmlZco2lM3J_3M-ZdxcPr7q-XT2RMQWdKrtH8xoxflo2CRbGU6yL2cZNEbPCNmWgBADzNj-Ue2c8JWQv_OqmbY
CitedBy_id crossref_primary_10_3390_sym9090191
crossref_primary_10_1002_fut_22012
crossref_primary_10_1016_j_asoc_2020_106898
crossref_primary_10_1016_j_eswa_2020_113447
crossref_primary_10_1007_s13042_021_01310_y
crossref_primary_10_1007_s40899_022_00744_8
crossref_primary_10_1016_j_eswa_2020_114056
crossref_primary_10_1007_s10489_021_02864_8
crossref_primary_10_1007_s10115_023_01875_w
crossref_primary_10_1016_j_knosys_2020_105937
crossref_primary_10_1016_j_asoc_2023_110356
crossref_primary_10_1007_s40314_021_01534_2
crossref_primary_10_2478_cait_2018_0001
crossref_primary_10_1016_j_eswa_2018_03_005
crossref_primary_10_1007_s40815_019_00688_w
crossref_primary_10_1109_ACCESS_2019_2901842
crossref_primary_10_1109_TFUZZ_2021_3113762
crossref_primary_10_1016_j_neucom_2017_02_054
crossref_primary_10_1007_s10614_021_10202_w
crossref_primary_10_1016_j_ins_2018_02_016
crossref_primary_10_1016_j_knosys_2016_11_019
crossref_primary_10_1016_j_ijar_2019_05_002
crossref_primary_10_18187_pjsor_v18i4_4212
crossref_primary_10_1016_j_ins_2016_09_027
crossref_primary_10_1016_j_ins_2021_02_024
crossref_primary_10_3390_sym9100207
crossref_primary_10_1002_for_2979
crossref_primary_10_1007_s10489_018_1189_z
crossref_primary_10_1016_j_asoc_2018_11_008
crossref_primary_10_1016_j_engappai_2018_02_015
crossref_primary_10_1002_for_2734
crossref_primary_10_3390_math11132800
crossref_primary_10_1016_j_asoc_2024_111759
crossref_primary_10_1016_j_ymssp_2023_110174
crossref_primary_10_1007_s00500_018_3335_2
crossref_primary_10_1007_s00521_017_3325_9
crossref_primary_10_1007_s00521_024_10727_9
crossref_primary_10_1016_j_ins_2019_08_058
crossref_primary_10_1007_s10489_021_02473_5
crossref_primary_10_1007_s10614_021_10132_7
crossref_primary_10_1109_TFUZZ_2019_2914642
crossref_primary_10_3390_sym9070124
crossref_primary_10_1186_s43093_022_00125_9
crossref_primary_10_1016_j_knosys_2020_106139
crossref_primary_10_1016_j_asoc_2017_11_011
crossref_primary_10_1016_j_neucom_2017_04_076
crossref_primary_10_1016_j_neucom_2019_12_113
crossref_primary_10_1007_s41066_018_0126_1
crossref_primary_10_1109_TFUZZ_2020_2972823
crossref_primary_10_1016_j_resourpol_2024_105446
crossref_primary_10_1007_s12530_022_09452_2
crossref_primary_10_1088_1742_6596_1613_1_012019
crossref_primary_10_1007_s10489_021_02926_x
crossref_primary_10_1007_s10489_021_02845_x
crossref_primary_10_1016_j_asoc_2017_01_043
crossref_primary_10_1016_j_engappai_2018_11_004
crossref_primary_10_1007_s00521_022_07138_z
crossref_primary_10_1007_s11071_018_4433_5
crossref_primary_10_7240_jeps_1573839
crossref_primary_10_1007_s40314_024_02950_w
crossref_primary_10_1016_j_knosys_2018_10_035
crossref_primary_10_1016_j_knosys_2019_105239
crossref_primary_10_1016_j_eswa_2020_114444
crossref_primary_10_1007_s12065_021_00656_0
crossref_primary_10_3233_JIFS_211405
crossref_primary_10_1016_j_ins_2019_01_071
crossref_primary_10_1109_ACCESS_2021_3050437
crossref_primary_10_3390_math11194022
crossref_primary_10_1016_j_asoc_2017_04_021
crossref_primary_10_1016_j_ins_2016_10_022
crossref_primary_10_1016_j_eswa_2022_116659
crossref_primary_10_3390_sym12060954
crossref_primary_10_1007_s11069_022_05764_3
crossref_primary_10_1080_16168658_2019_1631557
crossref_primary_10_1088_1742_6596_1114_1_012012
crossref_primary_10_3390_w15223910
crossref_primary_10_3233_IDA_220755
crossref_primary_10_1007_s12597_020_00455_8
crossref_primary_10_1016_j_eswa_2024_126298
Cites_doi 10.1016/j.physa.2004.07.024
10.1016/j.procs.2013.05.281
10.1016/S0169-2070(99)00048-5
10.1109/TSMCB.2012.2223815
10.1016/j.eswa.2010.02.049
10.1016/j.omega.2011.07.008
10.1016/j.eswa.2008.10.034
10.1016/j.camwa.2008.07.033
10.1016/j.ins.2010.08.026
10.1016/j.ins.2014.09.038
10.1016/j.eswa.2005.09.020
10.1002/int.20145
10.1016/j.eswa.2011.02.098
10.1016/S0165-0114(97)00121-8
10.1016/j.physa.2007.02.084
10.1016/j.eswa.2007.12.013
10.1016/j.eswa.2009.02.085
10.1016/j.eswa.2009.02.057
10.1016/j.knosys.2014.11.003
10.1016/j.eswa.2011.02.052
10.1016/j.ins.2013.06.005
10.1016/j.eswa.2007.05.016
10.1016/j.eswa.2010.03.063
10.1016/j.physa.2005.08.014
10.1109/TFUZZ.2006.876367
10.1016/0165-0114(93)90355-L
10.1109/TFUZZ.2010.2073712
10.1016/j.eswa.2006.12.013
10.1016/j.asoc.2014.01.022
10.1016/j.eswa.2009.05.081
10.1016/0165-0114(94)90152-X
10.1016/j.asoc.2008.09.002
10.1016/j.eswa.2008.09.040
10.1016/j.asoc.2012.09.024
10.1016/j.datak.2008.06.002
10.1016/j.matcom.2010.09.011
10.1109/TSMCB.2005.857093
10.1016/0165-0114(95)00220-0
10.1016/j.ins.2010.01.014
10.1016/0165-0114(93)90372-O
10.1109/TSMCB.2006.890303
10.1016/j.physa.2004.11.006
10.1016/j.physa.2008.01.099
10.1016/j.eswa.2008.04.001
ContentType Journal Article
Copyright 2016 Elsevier Inc.
Copyright_xml – notice: 2016 Elsevier Inc.
DBID RYH
AAYXX
CITATION
7SC
8FD
JQ2
L7M
L~C
L~D
DOI 10.1016/j.ins.2016.05.038
DatabaseName CiNii Complete
CrossRef
Computer and Information Systems Abstracts
Technology Research Database
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle CrossRef
Computer and Information Systems Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Advanced Technologies Database with Aerospace
ProQuest Computer Science Collection
Computer and Information Systems Abstracts Professional
DatabaseTitleList
Computer and Information Systems Abstracts
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Library & Information Science
EISSN 1872-6291
EndPage 57
ExternalDocumentID 10_1016_j_ins_2016_05_038
S002002551630370X
GroupedDBID --K
--M
--Z
-~X
.DC
.~1
0R~
1B1
1RT
1~.
1~5
4.4
457
4G.
5GY
5VS
7-5
71M
8P~
9JN
9JO
AAAKF
AABNK
AACTN
AAEDT
AAEDW
AAIAV
AAIKJ
AAKOC
AALRI
AAOAW
AAQFI
AARIN
AAXUO
AAYFN
ABAOU
ABBOA
ABFNM
ABJNI
ABMAC
ABUCO
ABYKQ
ACAZW
ACDAQ
ACGFS
ACRLP
ACZNC
ADBBV
ADEZE
ADGUI
ADTZH
AEBSH
AECPX
AEKER
AENEX
AFKWA
AFTJW
AGHFR
AGUBO
AGYEJ
AHHHB
AHJVU
AHZHX
AIALX
AIEXJ
AIGVJ
AIKHN
AITUG
AJBFU
AJOXV
ALMA_UNASSIGNED_HOLDINGS
AMFUW
AMRAJ
AOUOD
APLSM
ARUGR
AXJTR
BJAXD
BKOJK
BLXMC
CS3
DU5
EBS
EFJIC
EFLBG
EJD
EO8
EO9
EP2
EP3
F5P
FDB
FIRID
FNPLU
FYGXN
G-Q
GBLVA
GBOLZ
HAMUX
IHE
J1W
JJJVA
KOM
LG9
LY1
M41
MHUIS
MO0
MS~
N9A
O-L
O9-
OAUVE
OZT
P-8
P-9
P2P
PC.
Q38
RIG
ROL
RPZ
SDF
SDG
SDP
SES
SPC
SPCBC
SSB
SSD
SST
SSV
SSW
SSZ
T5K
TN5
TWZ
WH7
XPP
ZMT
~02
~G-
AATTM
AAXKI
AAYWO
ACVFH
ADCNI
AEIPS
AEUPX
AFPUW
AFXIZ
AGCQF
AGRNS
AIIUN
AKBMS
AKRWK
AKYEP
ANKPU
RYH
SSH
1OL
29I
AAAKG
AAQXK
AAYXX
ABEFU
ABWVN
ABXDB
ACNNM
ACRPL
ADJOM
ADMUD
ADNMO
ADVLN
AFFNX
AFJKZ
AGQPQ
AIGII
APXCP
ASPBG
AVWKF
AZFZN
BNPGV
CITATION
FEDTE
FGOYB
HLZ
HVGLF
HZ~
H~9
R2-
SBC
SDS
SEW
UHS
WUQ
YYP
ZY4
7SC
8FD
JQ2
L7M
L~C
L~D
ID FETCH-LOGICAL-c425t-2be3bbcaa95786394cb58d1feb52c3e5a33c3f23248b0e6bb60f67dca16c4543
IEDL.DBID .~1
ISSN 0020-0255
IngestDate Thu Jul 10 20:39:31 EDT 2025
Tue Jul 01 04:16:33 EDT 2025
Thu Apr 24 23:12:51 EDT 2025
Thu Jun 26 22:39:24 EDT 2025
Fri Feb 23 02:33:55 EST 2024
IsPeerReviewed true
IsScholarly true
Keywords Fuzzy time series
Technical analysis
Financial forecasting
Genetic algorithm
Language English
Japanese
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c425t-2be3bbcaa95786394cb58d1feb52c3e5a33c3f23248b0e6bb60f67dca16c4543
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ORCID 0000-0001-5256-210X
0000-0002-2664-8193
PQID 1835615989
PQPubID 23500
PageCount 17
ParticipantIDs proquest_miscellaneous_1835615989
crossref_citationtrail_10_1016_j_ins_2016_05_038
crossref_primary_10_1016_j_ins_2016_05_038
nii_cinii_1870865117809378048
elsevier_sciencedirect_doi_10_1016_j_ins_2016_05_038
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2016-11-01
2016-11-00
20161101
PublicationDateYYYYMMDD 2016-11-01
PublicationDate_xml – month: 11
  year: 2016
  text: 2016-11-01
  day: 01
PublicationDecade 2010
PublicationTitle Information Sciences
PublicationYear 2016
Publisher Elsevier Inc
Elsevier BV
Publisher_xml – name: Elsevier Inc
– name: Elsevier BV
References Huarng, Yu, Hsu (bib0026) 2007; 37
Chen, Wang, Pan (bib0018) 2009; 36
Chen, Chen (bib0030) 2011; 19
Sullivan, Woodall (bib0040) 1994; 64
Cai, Zhang, Zheng, Leung (bib0053) 2015; 74
Egrioglu,C.H. Aladag, Yolcu (bib0014) 2011; 38
Chen (bib0003) 1996; 81
Chen, Pan, Liu (bib0049) 2013; 43
Avazbeigi, Doulabi, Karimi (bib0034) 2010; 37
Chen, Manalu, Shih, Sheu, Liu (bib0031) 2011
Cheng, Chen, Wei (bib0017) 2010; 180
Cheng, Cheng, Wang (bib0020) 2008; 34
Yolcu, Egrioglu, Uslu (bib0012) 2009; 9
Song, Chissom (bib0001) 1993; 54
Kirkpatrick, Dahlquist, Analysis (bib0023) 2006
Wang, Wang, Zhang, Guo (bib0044) 2012; 40
Wei, Cheng, Wu (bib0050) 2014; 19
Kim, Min, Han (bib0032) 2006; 32
Jilani, Burney (bib0008) 2008; 387
Chen, Chung (bib0015) 2006; 21
Chen, Cheng, Teoh (bib0016) 2007; 380
Leu, Lee, Jou (bib0048) 2009; 36
Cai, Zhang, Wu, Leung (bib0047) 2013; 18
Hiemstra (bib0042) 1994; 3
Chen, Chen (bib0052) 2015; 45
Aladag, Basaran, Egrioglu (bib0010) 2009; 36
Lee, Wang, Chen, Leu (bib0025) 1996; 14
Chen, Kao (bib0046) 2013; 247
Egrioglu, Aladag, U. Yolcu, Uslu (bib0033) 2009; 36
Brown (bib0037) 1956
Yu, Huarng (bib0027) 2008; 34
Hwang, Chen, Lee (bib0004) 1998; 100
Zhang, Jiang, Li (bib0036) 2004; 1
Kazem, Sharifi, Hussain, Saberi, Hussain (bib0043) 2013; 13
Egrioglu, Aladag, Yolcu (bib0013) 2009; 36
Huarng (bib0006) 2006; 36
Chen, Chen (bib0051) 2015; 294
Aladag, Yolcu, Egrioglu (bib0011) 2010; 81
Stevenson, John (bib0024) 2009; 55
Lee, Efendi, Ismail (bib0005) 2009; 25
Li, Cheng, Lin (bib0022) 2008; 56
Wan, Zhang, Si (bib0054) 2014
Chen, Tanuwijaya (bib0019) 2011; 38
Yu, Huarng (bib0028) 2010; 37
Lakshman Naik, Ramesh, Manjula, Govardhan (bib0041) 2012; 3
Park, Lee, Song, Chun (bib0035) 2010; 37
Wang, Chen (bib0021) 2009; 36
Yu (bib0009) 2005; 346
Teoh, Cheng, Chu (bib0007) 2008; 67
Leung, Daouk, Chen (bib0038) 2000; 16
Huarng, Yu (bib0039) 2006; 363
Yu (bib0045) 2004; 349
Song, Chissom (bib0002) 1993; 54
Chen, Chang (bib0029) 2010; 180
Chen (10.1016/j.ins.2016.05.038_bib0019) 2011; 38
Chen (10.1016/j.ins.2016.05.038_bib0046) 2013; 247
Chen (10.1016/j.ins.2016.05.038_bib0029) 2010; 180
Chen (10.1016/j.ins.2016.05.038_bib0003) 1996; 81
Song (10.1016/j.ins.2016.05.038_bib0001) 1993; 54
Kim (10.1016/j.ins.2016.05.038_bib0032) 2006; 32
Chen (10.1016/j.ins.2016.05.038_bib0049) 2013; 43
Leung (10.1016/j.ins.2016.05.038_bib0038) 2000; 16
Hwang (10.1016/j.ins.2016.05.038_bib0004) 1998; 100
Huarng (10.1016/j.ins.2016.05.038_bib0006) 2006; 36
Yu (10.1016/j.ins.2016.05.038_bib0009) 2005; 346
Aladag (10.1016/j.ins.2016.05.038_bib0011) 2010; 81
Aladag (10.1016/j.ins.2016.05.038_bib0010) 2009; 36
Huarng (10.1016/j.ins.2016.05.038_bib0039) 2006; 363
Chen (10.1016/j.ins.2016.05.038_bib0051) 2015; 294
Brown (10.1016/j.ins.2016.05.038_bib0037) 1956
Chen (10.1016/j.ins.2016.05.038_bib0052) 2015; 45
Stevenson (10.1016/j.ins.2016.05.038_bib0024) 2009; 55
Song (10.1016/j.ins.2016.05.038_bib0002) 1993; 54
Egrioglu (10.1016/j.ins.2016.05.038_bib0013) 2009; 36
Cai (10.1016/j.ins.2016.05.038_bib0047) 2013; 18
Wang (10.1016/j.ins.2016.05.038_bib0021) 2009; 36
Park (10.1016/j.ins.2016.05.038_bib0035) 2010; 37
Lee (10.1016/j.ins.2016.05.038_bib0025) 1996; 14
Lakshman Naik (10.1016/j.ins.2016.05.038_bib0041) 2012; 3
Wang (10.1016/j.ins.2016.05.038_bib0044) 2012; 40
Lee (10.1016/j.ins.2016.05.038_bib0005) 2009; 25
Chen (10.1016/j.ins.2016.05.038_bib0015) 2006; 21
Cai (10.1016/j.ins.2016.05.038_bib0053) 2015; 74
Yolcu (10.1016/j.ins.2016.05.038_bib0012) 2009; 9
Hiemstra (10.1016/j.ins.2016.05.038_bib0042) 1994; 3
Zhang (10.1016/j.ins.2016.05.038_bib0036) 2004; 1
Wei (10.1016/j.ins.2016.05.038_bib0050) 2014; 19
Sullivan (10.1016/j.ins.2016.05.038_bib0040) 1994; 64
Egrioglu,C.H. Aladag (10.1016/j.ins.2016.05.038_bib0014) 2011; 38
Cheng (10.1016/j.ins.2016.05.038_bib0020) 2008; 34
Huarng (10.1016/j.ins.2016.05.038_bib0026) 2007; 37
Teoh (10.1016/j.ins.2016.05.038_bib0007) 2008; 67
Wan (10.1016/j.ins.2016.05.038_bib0054) 2014
Chen (10.1016/j.ins.2016.05.038_bib0030) 2011; 19
Avazbeigi (10.1016/j.ins.2016.05.038_bib0034) 2010; 37
Cheng (10.1016/j.ins.2016.05.038_bib0017) 2010; 180
Yu (10.1016/j.ins.2016.05.038_bib0027) 2008; 34
Yu (10.1016/j.ins.2016.05.038_bib0028) 2010; 37
Chen (10.1016/j.ins.2016.05.038_bib0031) 2011
Kazem (10.1016/j.ins.2016.05.038_bib0043) 2013; 13
Yu (10.1016/j.ins.2016.05.038_bib0045) 2004; 349
Kirkpatrick (10.1016/j.ins.2016.05.038_bib0023) 2006
Jilani (10.1016/j.ins.2016.05.038_bib0008) 2008; 387
Leu (10.1016/j.ins.2016.05.038_bib0048) 2009; 36
Chen (10.1016/j.ins.2016.05.038_bib0016) 2007; 380
Egrioglu (10.1016/j.ins.2016.05.038_bib0033) 2009; 36
Chen (10.1016/j.ins.2016.05.038_bib0018) 2009; 36
Li (10.1016/j.ins.2016.05.038_bib0022) 2008; 56
References_xml – start-page: 2301
  year: 2011
  end-page: 2306
  ident: bib0031
  article-title: A new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques
  publication-title: Proceedings of 2011 IEEE International Conference on Systems, Man, and Cybernetics
– volume: 64
  start-page: 279
  year: 1994
  end-page: 293
  ident: bib0040
  article-title: A comparison of fuzzy forecasting and Markov modeling
  publication-title: Fuzzy Sets Syst.
– volume: 54
  start-page: 269
  year: 1993
  end-page: 277
  ident: bib0001
  article-title: Fuzzy time series and its models
  publication-title: Fuzzy Sets Syst.
– volume: 55
  start-page: 154
  year: 2009
  end-page: 157
  ident: bib0024
  article-title: Fuzzy time series forecasting using percentage change as the universe of discourse, world academy of science
  publication-title: Eng. Technol.
– volume: 14
  start-page: 468
  year: 1996
  end-page: 477
  ident: bib0025
  article-title: Handling forecasting problem based on two-factors high-order fuzzy time series
  publication-title: IEEE Trans. Fuzzy Syst.
– volume: 36
  start-page: 7424
  year: 2009
  end-page: 7434
  ident: bib0013
  article-title: A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model
  publication-title: Expert Syst. Appl.
– volume: 34
  start-page: 1235
  year: 2008
  end-page: 1242
  ident: bib0020
  article-title: Multi-attribute fuzzy time series method based on fuzzy clustering
  publication-title: Expert Syst. Appl.
– volume: 56
  start-page: 3052
  year: 2008
  end-page: 3063
  ident: bib0022
  article-title: A FCM-based deterministic forecasting model for fuzzy time series
  publication-title: Comput. Math. Appl.
– volume: 363
  start-page: 481
  year: 2006
  end-page: 491
  ident: bib0039
  article-title: The application of neural networks to forecast fuzzy time series
  publication-title: Physica A
– volume: 81
  start-page: 311
  year: 1996
  end-page: 319
  ident: bib0003
  article-title: Forecasting enrollments based on fuzzy time series
  publication-title: Fuzzy Sets Syst.
– volume: 37
  start-page: 959
  year: 2010
  end-page: 967
  ident: bib0035
  article-title: TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization
  publication-title: Expert Syst. Appl.
– volume: 54
  start-page: 1
  year: 1993
  end-page: 9
  ident: bib0002
  article-title: Forecasting enrollments with fuzzy time series, Part I
  publication-title: Fuzzy Sets Syst.
– volume: 247
  start-page: 62
  year: 2013
  end-page: 71
  ident: bib0046
  article-title: TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines
  publication-title: Inf. Sci.
– volume: 294
  start-page: 227
  year: 2015
  end-page: 241
  ident: bib0051
  article-title: A hybrid fuzzy time series model based on granular computing for stock price forecasting
  publication-title: Inf. Sci.
– volume: 32
  start-page: 241
  year: 2006
  end-page: 247
  ident: bib0032
  article-title: An evolutionary approach to the combination of multiple classifiers to predict a stock price index
  publication-title: Expert Syst. Appl.
– volume: 37
  start-page: 5529
  year: 2010
  ident: bib0028
  article-title: Corrigendum to A bivariate fuzzy time series model to forecast the TAIEX
  publication-title: Expert Syst. Appl.
– volume: 349
  start-page: 609
  year: 2004
  end-page: 624
  ident: bib0045
  article-title: Weighted fuzzy time-series models for TAIEX forecasting
  publication-title: Physica A
– volume: 36
  start-page: 11070
  year: 2009
  end-page: 11076
  ident: bib0018
  article-title: Forecasting enrollments using automatic clustering techniques and fuzzy logic relationships
  publication-title: Expert Syst. Appl.
– volume: 19
  start-page: 86
  year: 2014
  end-page: 92
  ident: bib0050
  article-title: A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock
  publication-title: Appl. Soft Comput.
– volume: 180
  start-page: 1610
  year: 2010
  end-page: 1629
  ident: bib0017
  article-title: A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
  publication-title: Inf. Sci.
– volume: 100
  start-page: 217
  year: 1998
  end-page: 228
  ident: bib0004
  article-title: Handling forecasting problems using fuzzy time series
  publication-title: Fuzzy Sets Syst.
– volume: 38
  start-page: 10355
  year: 2011
  end-page: 10357
  ident: bib0014
  article-title: Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering
  publication-title: Expert Syst. Appl.
– volume: 36
  start-page: 10589
  year: 2009
  end-page: 10594
  ident: bib0033
  article-title: A new approach based on artificial neural networks for high order multivariate fuzzy time series
  publication-title: Expert Syst. Appl.
– volume: 36
  start-page: 4228
  year: 2009
  end-page: 4231
  ident: bib0010
  article-title: Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations
  publication-title: Expert Syst. Appl.
– volume: 16
  start-page: 173
  year: 2000
  end-page: 190
  ident: bib0038
  article-title: Forecasting stock indices: a comparison of classification and level estimation models
  publication-title: Int. J. Forecast.
– volume: 9
  start-page: 647
  year: 2009
  end-page: 651
  ident: bib0012
  article-title: A new approach for determining the length of intervals for fuzzy time series
  publication-title: Appl. Soft Comput.
– volume: 38
  start-page: 10594
  year: 2011
  end-page: 10605
  ident: bib0019
  article-title: Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques
  publication-title: Expert Syst. Appl.
– volume: 34
  start-page: 2945
  year: 2008
  end-page: 2952
  ident: bib0027
  article-title: A bivariate fuzzy time series model to forecast the TAIEX
  publication-title: Expert Syst. Appl.
– volume: 387
  start-page: 2857
  year: 2008
  end-page: 2862
  ident: bib0008
  article-title: A refined fuzzy time series model for stock market forecasting
  publication-title: Physica A
– volume: 180
  start-page: 4772
  year: 2010
  end-page: 4783
  ident: bib0029
  article-title: Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy interpolation techniques
  publication-title: Inf. Sci.
– volume: 37
  start-page: 5630
  year: 2010
  end-page: 5639
  ident: bib0034
  article-title: Choosing the appropriate order in fuzzy time series: a new N-factor fuzzy time series for prediction of the auto industry production
  publication-title: Expert Syst. Appl.
– volume: 346
  start-page: 657
  year: 2005
  end-page: 681
  ident: bib0009
  article-title: A refined fuzzy time-series model for forecasting
  publication-title: Physica A, Stat. Theor. Phys.
– volume: 36
  start-page: 8107
  year: 2009
  end-page: 8114
  ident: bib0048
  article-title: A distance-based fuzzy time series model for exchange rates forecasting
  publication-title: Expert Syst. Appl.
– volume: 40
  start-page: 758
  year: 2012
  end-page: 766
  ident: bib0044
  article-title: Stock index forecasting based on a hybrid model
  publication-title: Omega
– volume: 67
  start-page: 103
  year: 2008
  end-page: 117
  ident: bib0007
  article-title: Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets
  publication-title: Data Knowl. Eng.
– volume: 37
  start-page: 836
  year: 2007
  end-page: 846
  ident: bib0026
  article-title: A multivariate heuristic model for fuzzy time-series forecasting
  publication-title: IEEE Trans. Syst. Man Cybern. Part B Cybern.
– start-page: 15
  year: 1956
  ident: bib0037
  article-title: Exponential Smoothing for Predicting Demand
– volume: 45
  start-page: 405
  year: 2015
  end-page: 417
  ident: bib0052
  article-title: Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships
  publication-title: IEEE Trans. Cybern.
– volume: 25
  start-page: 67
  year: 2009
  end-page: 78
  ident: bib0005
  article-title: Modified Weighted for Enrollment Forecasting Based on Fuzzy Time Series
  publication-title: MATEMATIKA
– volume: 81
  start-page: 875
  year: 2010
  end-page: 882
  ident: bib0011
  article-title: A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks
  publication-title: Math. Comput. Simul
– volume: 3
  start-page: 162
  year: 2012
  end-page: 172
  ident: bib0041
  article-title: Prediction of Stock Market Index Using Genetic Algorithm
  publication-title: Comput. Eng. Intell. Syst.
– volume: 13
  start-page: 947
  year: 2013
  end-page: 958
  ident: bib0043
  article-title: Support vector regression with chaos-based firefly algorithm for stock market price forecasting
  publication-title: Appl. Soft Comput.
– start-page: 217
  year: 2014
  end-page: 223
  ident: bib0054
  article-title: Evolutionary computation with multi-variates hybrid multi-order fuzzy time series for stock forecasting
  publication-title: International Conference on Computational Science and Engineering
– volume: 380
  start-page: 377
  year: 2007
  end-page: 390
  ident: bib0016
  article-title: Fuzzy time-series based on Fibonacci sequence for stock price forecasting
  publication-title: Physica A
– year: 2006
  ident: bib0023
  article-title: The Complete Resource for Financial Market Technicians
– volume: 21
  start-page: 485
  year: 2006
  end-page: 501
  ident: bib0015
  article-title: Forecasting enrollments using high-order fuzzy time series and genetic algorithms
  publication-title: Int. J. Intell. Syst.
– volume: 1
  start-page: 106
  year: 2004
  end-page: 109
  ident: bib0036
  article-title: Application of neural networks in financial data mining
  publication-title: Int.J. Comput. Intell.
– volume: 43
  start-page: 1102
  year: 2013
  end-page: 1117
  ident: bib0049
  article-title: Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques
  publication-title: IEEE Trans. Cybern.
– volume: 3
  start-page: 281
  year: 1994
  end-page: 287
  ident: bib0042
  article-title: A stock market forecasting support system based on fuzzy logic
  publication-title: Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences
– volume: 36
  start-page: 328
  year: 2006
  end-page: 340
  ident: bib0006
  article-title: Ratio-based lengths of intervals to improve fuzzy time series forecasting
  publication-title: IEEE Trans. Syst. Man. Cybern. Part B Cybern.
– volume: 19
  start-page: 1
  year: 2011
  end-page: 12
  ident: bib0030
  article-title: TAIEX forecasting based on fuzzy time series and fuzzy variation groups
  publication-title: IEEE Trans. Fuzzy Syst.
– volume: 36
  start-page: 2143
  year: 2009
  end-page: 2154
  ident: bib0021
  article-title: Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factor high-order fuzzy time series
  publication-title: Expert Syst. Appl.
– volume: 18
  start-page: 1155
  year: 2013
  end-page: 1162
  ident: bib0047
  article-title: A novel stock forecasting model based on fuzzy time series and genetic algorithm
  publication-title: Proc. Comput. Sci.
– volume: 74
  start-page: 61
  year: 2015
  end-page: 68
  ident: bib0053
  article-title: A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression
  publication-title: Knowl. Based Syst.
– volume: 346
  start-page: 657
  year: 2005
  ident: 10.1016/j.ins.2016.05.038_bib0009
  article-title: A refined fuzzy time-series model for forecasting
  publication-title: Physica A, Stat. Theor. Phys.
  doi: 10.1016/j.physa.2004.07.024
– volume: 55
  start-page: 154
  year: 2009
  ident: 10.1016/j.ins.2016.05.038_bib0024
  article-title: Fuzzy time series forecasting using percentage change as the universe of discourse, world academy of science
  publication-title: Eng. Technol.
– volume: 18
  start-page: 1155
  year: 2013
  ident: 10.1016/j.ins.2016.05.038_bib0047
  article-title: A novel stock forecasting model based on fuzzy time series and genetic algorithm
  publication-title: Proc. Comput. Sci.
  doi: 10.1016/j.procs.2013.05.281
– volume: 16
  start-page: 173
  year: 2000
  ident: 10.1016/j.ins.2016.05.038_bib0038
  article-title: Forecasting stock indices: a comparison of classification and level estimation models
  publication-title: Int. J. Forecast.
  doi: 10.1016/S0169-2070(99)00048-5
– volume: 43
  start-page: 1102
  year: 2013
  ident: 10.1016/j.ins.2016.05.038_bib0049
  article-title: Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TSMCB.2012.2223815
– volume: 37
  start-page: 5630
  year: 2010
  ident: 10.1016/j.ins.2016.05.038_bib0034
  article-title: Choosing the appropriate order in fuzzy time series: a new N-factor fuzzy time series for prediction of the auto industry production
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2010.02.049
– volume: 40
  start-page: 758
  year: 2012
  ident: 10.1016/j.ins.2016.05.038_bib0044
  article-title: Stock index forecasting based on a hybrid model
  publication-title: Omega
  doi: 10.1016/j.omega.2011.07.008
– volume: 36
  start-page: 8107
  year: 2009
  ident: 10.1016/j.ins.2016.05.038_bib0048
  article-title: A distance-based fuzzy time series model for exchange rates forecasting
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2008.10.034
– volume: 56
  start-page: 3052
  year: 2008
  ident: 10.1016/j.ins.2016.05.038_bib0022
  article-title: A FCM-based deterministic forecasting model for fuzzy time series
  publication-title: Comput. Math. Appl.
  doi: 10.1016/j.camwa.2008.07.033
– volume: 180
  start-page: 4772
  year: 2010
  ident: 10.1016/j.ins.2016.05.038_bib0029
  article-title: Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy interpolation techniques
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2010.08.026
– volume: 294
  start-page: 227
  year: 2015
  ident: 10.1016/j.ins.2016.05.038_bib0051
  article-title: A hybrid fuzzy time series model based on granular computing for stock price forecasting
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2014.09.038
– volume: 3
  start-page: 162
  year: 2012
  ident: 10.1016/j.ins.2016.05.038_bib0041
  article-title: Prediction of Stock Market Index Using Genetic Algorithm
  publication-title: Comput. Eng. Intell. Syst.
– start-page: 15
  year: 1956
  ident: 10.1016/j.ins.2016.05.038_bib0037
– volume: 32
  start-page: 241
  year: 2006
  ident: 10.1016/j.ins.2016.05.038_bib0032
  article-title: An evolutionary approach to the combination of multiple classifiers to predict a stock price index
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2005.09.020
– volume: 21
  start-page: 485
  year: 2006
  ident: 10.1016/j.ins.2016.05.038_bib0015
  article-title: Forecasting enrollments using high-order fuzzy time series and genetic algorithms
  publication-title: Int. J. Intell. Syst.
  doi: 10.1002/int.20145
– volume: 38
  start-page: 10594
  year: 2011
  ident: 10.1016/j.ins.2016.05.038_bib0019
  article-title: Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2011.02.098
– volume: 100
  start-page: 217
  year: 1998
  ident: 10.1016/j.ins.2016.05.038_bib0004
  article-title: Handling forecasting problems using fuzzy time series
  publication-title: Fuzzy Sets Syst.
  doi: 10.1016/S0165-0114(97)00121-8
– volume: 380
  start-page: 377
  year: 2007
  ident: 10.1016/j.ins.2016.05.038_bib0016
  article-title: Fuzzy time-series based on Fibonacci sequence for stock price forecasting
  publication-title: Physica A
  doi: 10.1016/j.physa.2007.02.084
– volume: 36
  start-page: 2143
  year: 2009
  ident: 10.1016/j.ins.2016.05.038_bib0021
  article-title: Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factor high-order fuzzy time series
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2007.12.013
– volume: 25
  start-page: 67
  year: 2009
  ident: 10.1016/j.ins.2016.05.038_bib0005
  article-title: Modified Weighted for Enrollment Forecasting Based on Fuzzy Time Series
  publication-title: MATEMATIKA
– volume: 36
  start-page: 11070
  year: 2009
  ident: 10.1016/j.ins.2016.05.038_bib0018
  article-title: Forecasting enrollments using automatic clustering techniques and fuzzy logic relationships
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2009.02.085
– volume: 36
  start-page: 10589
  year: 2009
  ident: 10.1016/j.ins.2016.05.038_bib0033
  article-title: A new approach based on artificial neural networks for high order multivariate fuzzy time series
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2009.02.057
– volume: 74
  start-page: 61
  year: 2015
  ident: 10.1016/j.ins.2016.05.038_bib0053
  article-title: A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression
  publication-title: Knowl. Based Syst.
  doi: 10.1016/j.knosys.2014.11.003
– volume: 38
  start-page: 10355
  year: 2011
  ident: 10.1016/j.ins.2016.05.038_bib0014
  article-title: Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2011.02.052
– volume: 247
  start-page: 62
  year: 2013
  ident: 10.1016/j.ins.2016.05.038_bib0046
  article-title: TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2013.06.005
– volume: 3
  start-page: 281
  year: 1994
  ident: 10.1016/j.ins.2016.05.038_bib0042
  article-title: A stock market forecasting support system based on fuzzy logic
– volume: 34
  start-page: 2945
  year: 2008
  ident: 10.1016/j.ins.2016.05.038_bib0027
  article-title: A bivariate fuzzy time series model to forecast the TAIEX
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2007.05.016
– year: 2006
  ident: 10.1016/j.ins.2016.05.038_bib0023
– volume: 37
  start-page: 5529
  year: 2010
  ident: 10.1016/j.ins.2016.05.038_bib0028
  article-title: Corrigendum to A bivariate fuzzy time series model to forecast the TAIEX
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2010.03.063
– volume: 363
  start-page: 481
  year: 2006
  ident: 10.1016/j.ins.2016.05.038_bib0039
  article-title: The application of neural networks to forecast fuzzy time series
  publication-title: Physica A
  doi: 10.1016/j.physa.2005.08.014
– volume: 14
  start-page: 468
  year: 1996
  ident: 10.1016/j.ins.2016.05.038_bib0025
  article-title: Handling forecasting problem based on two-factors high-order fuzzy time series
  publication-title: IEEE Trans. Fuzzy Syst.
  doi: 10.1109/TFUZZ.2006.876367
– volume: 54
  start-page: 1
  year: 1993
  ident: 10.1016/j.ins.2016.05.038_bib0002
  article-title: Forecasting enrollments with fuzzy time series, Part I
  publication-title: Fuzzy Sets Syst.
  doi: 10.1016/0165-0114(93)90355-L
– volume: 19
  start-page: 1
  year: 2011
  ident: 10.1016/j.ins.2016.05.038_bib0030
  article-title: TAIEX forecasting based on fuzzy time series and fuzzy variation groups
  publication-title: IEEE Trans. Fuzzy Syst.
  doi: 10.1109/TFUZZ.2010.2073712
– volume: 34
  start-page: 1235
  year: 2008
  ident: 10.1016/j.ins.2016.05.038_bib0020
  article-title: Multi-attribute fuzzy time series method based on fuzzy clustering
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2006.12.013
– volume: 19
  start-page: 86
  year: 2014
  ident: 10.1016/j.ins.2016.05.038_bib0050
  article-title: A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2014.01.022
– volume: 37
  start-page: 959
  year: 2010
  ident: 10.1016/j.ins.2016.05.038_bib0035
  article-title: TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2009.05.081
– volume: 64
  start-page: 279
  year: 1994
  ident: 10.1016/j.ins.2016.05.038_bib0040
  article-title: A comparison of fuzzy forecasting and Markov modeling
  publication-title: Fuzzy Sets Syst.
  doi: 10.1016/0165-0114(94)90152-X
– start-page: 217
  year: 2014
  ident: 10.1016/j.ins.2016.05.038_bib0054
  article-title: Evolutionary computation with multi-variates hybrid multi-order fuzzy time series for stock forecasting
– volume: 9
  start-page: 647
  year: 2009
  ident: 10.1016/j.ins.2016.05.038_bib0012
  article-title: A new approach for determining the length of intervals for fuzzy time series
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2008.09.002
– volume: 36
  start-page: 7424
  year: 2009
  ident: 10.1016/j.ins.2016.05.038_bib0013
  article-title: A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2008.09.040
– volume: 13
  start-page: 947
  year: 2013
  ident: 10.1016/j.ins.2016.05.038_bib0043
  article-title: Support vector regression with chaos-based firefly algorithm for stock market price forecasting
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2012.09.024
– volume: 67
  start-page: 103
  year: 2008
  ident: 10.1016/j.ins.2016.05.038_bib0007
  article-title: Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets
  publication-title: Data Knowl. Eng.
  doi: 10.1016/j.datak.2008.06.002
– volume: 81
  start-page: 875
  year: 2010
  ident: 10.1016/j.ins.2016.05.038_bib0011
  article-title: A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks
  publication-title: Math. Comput. Simul
  doi: 10.1016/j.matcom.2010.09.011
– volume: 36
  start-page: 328
  year: 2006
  ident: 10.1016/j.ins.2016.05.038_bib0006
  article-title: Ratio-based lengths of intervals to improve fuzzy time series forecasting
  publication-title: IEEE Trans. Syst. Man. Cybern. Part B Cybern.
  doi: 10.1109/TSMCB.2005.857093
– volume: 81
  start-page: 311
  year: 1996
  ident: 10.1016/j.ins.2016.05.038_bib0003
  article-title: Forecasting enrollments based on fuzzy time series
  publication-title: Fuzzy Sets Syst.
  doi: 10.1016/0165-0114(95)00220-0
– volume: 180
  start-page: 1610
  year: 2010
  ident: 10.1016/j.ins.2016.05.038_bib0017
  article-title: A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2010.01.014
– start-page: 2301
  year: 2011
  ident: 10.1016/j.ins.2016.05.038_bib0031
  article-title: A new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques
– volume: 45
  start-page: 405
  year: 2015
  ident: 10.1016/j.ins.2016.05.038_bib0052
  article-title: Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships
  publication-title: IEEE Trans. Cybern.
– volume: 54
  start-page: 269
  year: 1993
  ident: 10.1016/j.ins.2016.05.038_bib0001
  article-title: Fuzzy time series and its models
  publication-title: Fuzzy Sets Syst.
  doi: 10.1016/0165-0114(93)90372-O
– volume: 1
  start-page: 106
  year: 2004
  ident: 10.1016/j.ins.2016.05.038_bib0036
  article-title: Application of neural networks in financial data mining
  publication-title: Int.J. Comput. Intell.
– volume: 37
  start-page: 836
  year: 2007
  ident: 10.1016/j.ins.2016.05.038_bib0026
  article-title: A multivariate heuristic model for fuzzy time-series forecasting
  publication-title: IEEE Trans. Syst. Man Cybern. Part B Cybern.
  doi: 10.1109/TSMCB.2006.890303
– volume: 349
  start-page: 609
  year: 2004
  ident: 10.1016/j.ins.2016.05.038_bib0045
  article-title: Weighted fuzzy time-series models for TAIEX forecasting
  publication-title: Physica A
  doi: 10.1016/j.physa.2004.11.006
– volume: 387
  start-page: 2857
  year: 2008
  ident: 10.1016/j.ins.2016.05.038_bib0008
  article-title: A refined fuzzy time series model for stock market forecasting
  publication-title: Physica A
  doi: 10.1016/j.physa.2008.01.099
– volume: 36
  start-page: 4228
  year: 2009
  ident: 10.1016/j.ins.2016.05.038_bib0010
  article-title: Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2008.04.001
SSID ssj0004766
ssib006542140
ssib007615888
ssib006542138
ssib000462208
ssib017384996
ssib002620769
Score 2.5170374
Snippet •The directional accuracy rate of forecasting model is first selected as performance measures.•Exponential smoothing is used to eliminate noise in the time...
Financial trading is one of the most common risk investment actions in the modern economic environment because financial market systems are complex non-linear...
SourceID proquest
crossref
nii
elsevier
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 41
SubjectTerms Dynamical systems
Error analysis
Financial forecasting
Forecasting
Fuzzy
Fuzzy time series
Genetic algorithm
Genetic algorithms
Mean square values
Nonlinear dynamics
Technical analysis
Time series
Title A novel forecasting method based on multi-order fuzzy time series and technical analysis
URI https://dx.doi.org/10.1016/j.ins.2016.05.038
https://cir.nii.ac.jp/crid/1870865117809378048
https://www.proquest.com/docview/1835615989
Volume 367-368
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NSxwxFA9bvdiDWD-oH7tEKD0I0WSSycweF6lslXqysLeQZDIwRWalu1vQg3-772UyWrF46GUgkxcmk_f1g7wPQr5g-1eZW8nKUnqmVKiZy3xgUmmhuRPCx5YsP6719Ke6nOWzATnvc2EwrDLZ_s6mR2ud3pyl0zy7axrM8c0iIgZEwWXBZ5jBrgqU8tPHlzAPeKO7MA_OkLq_2YwxXk2LFbuFjsU7MUXl377pQ9s0b2x1dEAXW2QzIUc66Tb3iQxCu00-_lVPcJsMUxYC_UpTmhEeO036u0NmE9rO_4RbCnPB2wWGPNOuhzRFd1ZRoI4hhiyW5KT16uHhnmL_eYqiGhbUthXt6r4Cc2HUlTTZJTcX327Opyy1VmAelHTJMhekc97aMWgsgBTlXV5Wog4uz7wMwDzpZY1oq3Q8aOc0r3VReSu0V7mSe2StnbfhM6Hg7epxlYkQMqtkYV1dgh0IThdcVLao9wnvz9T4VHYcu1_cmj6-7JcBNhhkg-G5ATbsk5PnJXddzY33iFXPKPNKcAz4hPeWDYGpsCN8CjBapQboWZQc4Br8AMwf9-w2oHB4i2LbMF8tgFgC5szH5fjg_z59SDZw1CU0HpG15e9VGAKyWbpRFN0RWZ98v5pePwHO__VI
linkProvider Elsevier
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1NT9wwEB0BPbQ9IEpblcIWV6p6qJRix46TPSIEWihw2kp7s2zHkYJQFnV3K5UDv50Zx4FWrTj0EimxrTh-npkXeT4APlH5V1lYmVWV9JlSoclc7kMmlRaaOyF8LMlycakn39XZrJitwdEQC0NulUn39zo9auv05CCt5sFN21KMbx4ZMTIKLks-W4dnCsWXyhh8vXv081Blf2BJ_0nUfTjajE5ebUcpu4WO2TspRuXfxmm9a9u_lHW0QCdbsJmoIzvsZ_cK1kK3DS9_Syi4DaMUhsA-sxRnROvOkgC_htkh6-Y_wzXDtuDtgnyeWV9EmpE9qxn2jj6GWczJyZrV7e0vRgXoGe3VsGC2q1mf-BXRxbs-p8kbmJ4cT48mWaqtkHmU0mWWuyCd89aOUWSRpSjviqoWTXBF7mVA9KSXDdGtyvGgndO80WXtrdBeFUq-hY1u3oV3wNDcNeM6FyHkVsnSuqZCRRCcLrmobdnsAB_W1PiUd5zKX1ybwcHsyiAMhmAwvDAIww58eRhy0yfdeKqzGoAyf-wcg0bhqWEjBBVnRFeBWqvSyD3LiiNfww_A9o8D3AYljo5RbBfmqwV2lkg6i3E1fv9_r96H55Ppxbk5P738tgsvqKWPbtyDjeWPVRghzVm6D3Eb3wNpnfbW
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+novel+forecasting+method+based+on+multi-order+fuzzy+time+series+and+technical+analysis&rft.jtitle=Information+sciences&rft.au=Ye%2C+Furong&rft.au=Zhang%2C+Liming&rft.au=Zhang%2C+Defu&rft.au=Fujita%2C+Hamido&rft.date=2016-11-01&rft.issn=0020-0255&rft.volume=367&rft.spage=41&rft.epage=57&rft_id=info:doi/10.1016%2Fj.ins.2016.05.038&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-0255&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-0255&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-0255&client=summon