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Soft Computing

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22.06.2018 | Methodologies and Application

A novel method based on FTS with both GA-FCM and multifactor BPNN for stock forecasting

verfasst von: Wenyu Zhang, Shixiong Zhang, Shuai Zhang, Dejian Yu, NingNing Huang

Erschienen in: Soft Computing | Ausgabe 16/2019

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Abstract

The fuzzy time series (FTS) model has been proposed for many years, and many researches have been conducted to improve or enhance the model. This study proposed a novel method for stock forecasting, which is based on FTS forecasting with genetic algorithm (GA)-fuzzy C-means (FCM) and multifactor back-propagation neural networks (BPNN). The GA algorithm is utilized to alleviate the FCM’s issue of falling into local optimum in the process of partitioning the universe of discourse and fuzzifying the time series. The multifactor BPNN considers relatively more information to train the neural networks and then forecast new stock index fluctuations. Finally, the proposed method is compared with other previous research methods by using SSECI and TAIEX data to verify the proposed method’s effectiveness and efficiency in forecasting financial time series.

Vorheriger Artikel Knowledge measure for interval-valued intuitionistic fuzzy sets and its application to decision making under uncertainty

Nächster Artikel RETRACTED ARTICLE: Application of support vector neural network with variational mode decomposition for exchange rate forecasting

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Aladag CH, Basaran MA, Egrioglu E et al (2009) Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. Expert Syst Appl 36(3):4228–4231CrossRef

Altiparmak F, Gen M, Lin L et al (2006) A genetic algorithm approach for multi-objective optimization of supply chain networks. Comput Ind Eng 51(1):196–215CrossRef

Armano G, Marchesi M, Murru A (2005) A hybrid genetic-neural architecture for stock indexes forecasting. Inf Sci 170(1):3–33MathSciNetCrossRef

Askari S, Montazerin N (2015) A high-order multi-variable fuzzy time series forecasting algorithm based on fuzzy clustering. Expert Syst Appl 42(4):2121–2135CrossRef

Askari S, Montazerin N, Zarandi MHF (2015) A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables. Appl Soft Comput 35:151–160CrossRef

Athanasopoulos G, Ashton S (2012) Multivariate exponential smoothing for forecasting tourist arrivals. J Travel Res 51(5):640–652CrossRef

Atsalakis GS, Valavanis KP (2009) Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Syst Appl 36(7):10696–10707CrossRef

Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, DordrechtMATHCrossRef

Cai QS, Zhang D, Wu B et al (2013) A novel stock forecasting model based on fuzzy time series and genetic algorithm. Proc Comput Sci 18:1155–1162CrossRef

Cai Q, Zhang D, Zheng W et al (2015) A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowl Based Syst 74:61–68CrossRef

Che JX (2014) A novel hybrid model for bi-objective short-term electric load forecasting. Int J Electr Power Energy Syst 61:259–266CrossRef

Chen SM (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst 81(3):311–319MathSciNetCrossRef

Chen MY, Chen BT (2014) Online fuzzy time series analysis based on entropy discretization and a Fast Fourier Transform. Appl Soft Comput 14:156–166CrossRef

Chen MY, Chen BT (2015) A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf Sci 294:227–241MathSciNetCrossRef

Chen SM, Tanuwijaya K (2011) Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques. Expert Syst Appl 38(12):15425–15437CrossRef

Cheng CH, Cheng GW, Wang JW (2008) Multi-attribute fuzzy time series method based on fuzzy clustering. Expert Syst Appl 34(2):1235–1242CrossRef

Egrioglu E, Aladag CH, Yolcu U et al (2011) Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering. Expert Syst Appl 38(8):10355–10357CrossRef

Fogel DB (1994) An introduction to simulated evolutionary optimization. IEEE Trans Neural Netw 5(1):3–14CrossRef

Guo Z, Wu J, Lu H et al (2011) A case study on a hybrid wind speed forecasting method using BP neural network. Knowl Based Syst 24(7):1048–1056CrossRef

Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann ArborMATH

Hong WC (2011) Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 74(12):2096–2107CrossRef

Hong WC, Dong Y, Chen LY et al (2011) SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Appl Soft Comput 11(2):1881–1890CrossRef

Huang YL, Horng SJ, Kao TW et al (2011) An improved forecasting model based on the weighted fuzzy relationship matrix combined with a PSO adaptation for enrollments. Int J Innov Comput Inf Control 7(7A):4027–4046

Huarng K, Yu THK (2006) The application of neural networks to forecast fuzzy time series. Physica A 363(2):481–491CrossRef

Lee WJ, Hong J (2015) A hybrid dynamic and fuzzy time series model for mid-term power load forecasting. Int J Electr Power Energy Syst 64:1057–1062CrossRef

Mai T, Ghosh B, Wilson S (2014) Short-term traffic-flow forecasting with auto-regressive moving average models. Proc Inst Civ Eng Transp 167(4):232–239

Pappas SS, Ekonomou L, Karamousantas DC et al (2008) Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models. Energy 33(9):1353–1360CrossRef

Park JH, Park YM, Lee KY (1991) Composite modeling for adaptive short-term load forecasting. IEEE Trans Power Syst 6(2):450–457CrossRef

Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. Cognit Model 5(3):1MATH

Saab S, Badr E, Nasr G (2001) Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon. Energy 26(1):1–14CrossRef

Song Q, Chissom BS (1993a) Fuzzy time series and its models. Fuzzy Sets Syst 54(3):269–277MathSciNetMATHCrossRef

Song Q, Chissom BS (1993b) Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst 54(1):1–9CrossRef

Xiao Z, Ye SJ, Zhong B et al (2009) BP neural network with rough set for short term load forecasting. Expert Syst Appl 36(1):273–279CrossRef

Ye F, Zhang L, Zhang D et al (2016) A novel forecasting method based on multi-order fuzzy time series and technical analysis. Inf Sci 367:41–57CrossRefMATH

Yolcu OC, Yolcu U, Egrioglu E et al (2016) High order fuzzy time series forecasting method based on an intersection operation. Appl Math Model 40(19):8750–8765MathSciNetCrossRef

Yu L, Wang S, Wen F et al (2012) Genetic algorithm-based multi-criteria project portfolio selection. Ann Oper Res 197(1):71–86MathSciNetMATHCrossRef

Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353MATHCrossRef

Zhang WY, Zhang SX, Zhang S et al (2017) A multi-factor and high-order stock forecast model based on type-2 FTS using cuckoo search and self-adaptive harmony search. Neurocomputing 240:13–24CrossRef

Titel: A novel method based on FTS with both GA-FCM and multifactor BPNN for stock forecasting
verfasst von: Wenyu Zhang
Shixiong Zhang
Shuai Zhang
Dejian Yu
NingNing Huang
Publikationsdatum: 22.06.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 16/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3335-2

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