关于股票价格的二阶模糊时间序列

doi:10.12068/j.issn.1005-3026.2019.02.028

东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (2): 300-304.DOI: 10.12068/j.issn.1005-3026.2019.02.028

• 管理科学 • 上一篇

关于股票价格的二阶模糊时间序列

刘智^1,2，张铁¹，董莹³，徐爽爽²

(1. 东北大学理学院，辽宁沈阳110819; 2. 沈阳工业大学基础部，辽宁辽阳111003; 3. 大连民族大学理学院，辽宁大连116600)

收稿日期:2017-12-06 修回日期:2017-12-06 出版日期:2019-02-15 发布日期:2019-02-12
通讯作者: 刘智
作者简介:刘智(1979-)，女，辽宁辽阳人，沈阳工业大学讲师，东北大学博士研究生；张铁(1956-)，男，辽宁沈阳人，东北大学教授，博士生导师.
基金资助:
国家自然科学基金专项基金天元访问学者项目(11726616)；辽宁省博士科研启动基金资助项目(201501164)；中央高校基本科研业务费专项资金资助项目 (DC201501040).

A Second-Order Fuzzy Time Series Model for Stock Price Analysis

LIU Zhi^1,2， ZHANG Tie¹， DONG Ying³， XU Shuang-shuang²

1. School of Sciences， Northeastern University， Shenyang 110819， China; 2. Department of Basics， Shenyang University of Technology， Liaoyang 111003， China; 3. College of Science， Dalian Nationalities University， Dalian 116600， China.

Received:2017-12-06 Revised:2017-12-06 Online:2019-02-15 Published:2019-02-12
Contact: LIU Zhi
About author:-
Supported by:
-

摘要/Abstract

摘要： 由于股票价格的时间序列具有不确定性，股市的真实模型不容易建立，而模糊时间序列在解决模糊性数据和不确定性数据方面具有较大优势;因此，本文首先将数据进行预处理并改进论域划分的方法，然后利用三角隶属度函数进行数据的模糊化处理，再利用模糊化后的数据建立三层BP神经网络，最后，应用广义的逆模糊数公式将预测模糊集进行逆模糊化，从而得到预测结果.应用本文方法对印度国家银行(SBI)股票价格和Alabama 大学的入学人数进行预测，预测结果精度较高.

关键词: 二阶模糊时间序列, BP神经网络, 逆模糊数, 模糊时间序列, 股票价格

Abstract: It is difficult to model stock market because of its uncertainty， while fuzzy time series has its advantages in dealing with fuzzy and uncertainty data. Accordingly， the data was first preprocessed and a new way to divide the universe of discourse was given， after which the data was fuzzified using the triangular membership function， and a three-layer BP neural network was then established according to the fuzzified data. Finally， the generalized inverse fuzzy number formula was used to defuzzify the fuzzy relation， with the prediction results obtained. The method was used for predicting the stock price of State Bank of India(SBI)and the enrollment of the University of Alabama， and the results showed that the prediction accuracy is higher than that of the related previous methods.

Key words: second-order fuzzy time series, BP neural network, inverse fuzzy number, fuzzy time series, stock price

中图分类号:

O159

刘智，张铁，董莹，徐爽爽. 关于股票价格的二阶模糊时间序列[J]. 东北大学学报:自然科学版, 2019, 40(2): 300-304.

LIU Zhi， ZHANG Tie， DONG Ying， XU Shuang-shuang. A Second-Order Fuzzy Time Series Model for Stock Price Analysis[J]. Journal of Northeastern University Natural Science, 2019, 40(2): 300-304.

参考文献

[1]Zadeh L A.Fuzzy sets［J］.Information Control，1965，8(3):338-353.
[2]Chang S S L，Zadeh L A.On fuzzy mapping and control［J］.IEEE Transactions on Systems，Man，and Cybernetics，1972，2:30-34.
[3]Dubois D J.Fuzzy sets and systems:theory and applications［M］.［S.l.］:Academic Press，1980:10-79.
[4]Saeidifar A，Pasha E.The possibilistic moments of fuzzy numbers and their applications［J］.Journal of Computational and Applied Mathematics，2009，223(2):1028-1042.
[5]Xu J P，Liu Q，Wang R.A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor［J］.Information Sciences，2008，178(8):2022-2043.
[6]Panigrahi M，Panda G，Nanda S.Convex fuzzy mapping with differentiability and its application in fuzzy optimization［J］.European Journal of Operational Research，2008，185(1):47-62.
[7]Lee C S，Wang M H.Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition［J］.Expert Systems with Applications， 2007，33(3):606-619.
[8]Yager R R.On ordered weighted averaging aggregation operators in multicriteria decision making［J］.IEEE Transactions on Systems，Man，and Cybernetics，1988，18(1):183-190.
[9]Chrysafis K A，Papadopoulos B K.On the fuzzy difference equations of finance［J］.Fuzzy Sets and Systems，2008，159(24):3259-3270.
[10]Aldrich J.Correlations genuine and spurious in Pearson and Yule［J］. Statistical Science，1995，10(4):364-376.
[11]Box G E P，Jenkins G M，Reinsel G C，et al.Time series analysis:forecasting and control［J］.Journal of the Operational Research Society， 1975，22(2):199-201.
[12]Anderson O.The elimination of spurious correlation due to position in time or space［J］.Biometrika，1914，10(2/3):269-279.
[13]Song Q，Chissom B S.Forecasting enrollments with fuzzy time series—part I［J］.Fuzzy Sets and Systems，1993，54(1):1-9.
[14]Hebb D.The organization of behavior［M］.［S.l.］:Wiley，1949:1-48.
[15]Chen S M.Forecasting enrollments based on fuzzy time series［J］.Fuzzy Sets and Systems，1996，81(3):311-319.
[16]Chen S M.Forecasting enrollments based on high-order fuzzy time series［J］.Cybernetic Systems，2002，33(1):1-16.
[17]Yu H K.Weighted fuzzy time series models for TAIEX forecasting［J］.Physica A，2005，349(3/4):609-624.
[18]Singh P，Borah B.High-order fuzzy neuroexpert system for time series forecasting［J］.Knowledge Based Systems 2016，46:12-21.
[19]Jilani T A，Burney S M A，Ardil C.Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning［J］.Proceedings of World Academy of Science:Engineering and Technology，2007，23(2):112-117.(上接第299页)
[4]Vapnik V N.The nature of statistical learning theory ［M］.Berlin:Springer-Verlag，1995:1-50.
[5]Hsu C W， Chang C C，Lin C J.A practical guide to support vector classification［R］.Taibei:University of National Taiwan，2003:1-12.
[6]Huang C L，Wang C J.A GA-based feature selection and parameters optimization for support vector machine［J］.Expert Systems with Applications，2006，31(2):231-240.
[7]Zhang K，Li C，Wang Y，et al.Collaborative support vector machine for malware detection［J］.Procedia Computer Science，2017，108:1682-1691.
[8]Tomar D，Agarwal S.Twin support vector machine:a review from 2007 to 2014［J］.Egyptian Informatics Journal，2015，16(1):55-69.
[9]Bamakan S M H，Wang H D，Ravasan A Z.Parameters optimization for nonparallel support vector machine by particle swarm optimization［J］.Procedia Computer Science，2016，91:482-491.
[10]陈建飞，蒋刚，杨剑锋.改进ABC-SVM的参数优化及应用［J］.机械设计与制造，2016，52(1):24-28.(Chen Jian-fei，Jiang Gang，Yang Jian-feng.Improved ABC-SVM parameter optimization and application［J］.Machinery Design and Manufacture，2016，52(1):24-28.)

关于股票价格的二阶模糊时间序列

A Second-Order Fuzzy Time Series Model for Stock Price Analysis

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张禹，何楷文，李清书，巩亚东. 面向STEP-NC自由曲面特征的加工操作方法智能决策[J]. 东北大学学报（自然科学版）, 2022, 43(7): 981-987.
[2]	季策，张晓. 基于GSA-BP神经网络的OFDM系统信道估计算法[J]. 东北大学学报（自然科学版）, 2022, 43(6): 769-775.
[3]	杨博文，霍军周，张伟，张占葛. 服役结构超前载荷实时预测方法的研究[J]. 东北大学学报（自然科学版）, 2022, 43(4): 541-550.
[4]	陈兵，韩烬阳，唐晓垒，夏搏然. 基于机器学习的拉矫延伸率预测模型及数值分析[J]. 东北大学学报（自然科学版）, 2022, 43(2): 236-242.
[5]	杨周，姜超，张义民，姜红猛. 采煤机截割部扭矩轴的动态可靠性分析[J]. 东北大学学报:自然科学版, 2020, 41(2): 217-222.
[6]	张华伟，郑晓涛. 基于遗传算法优化神经网络的拼焊板压边力预测[J]. 东北大学学报:自然科学版, 2020, 41(2): 241-245.
[7]	姜尚伟，金秀. 空间交互视角下投资者情绪对股价的影响[J]. 东北大学学报:自然科学版, 2020, 41(1): 143-147.
[8]	王鑫，顾亮，李晓雷，董明明. 基于系统响应的履带车辆路面识别方法[J]. 东北大学学报:自然科学版, 2019, 40(7): 968-973.
[9]	刘莹，于淼，刘阳. 基于乘客感知的公交车线路服务质量评价模型[J]. 东北大学学报:自然科学版, 2019, 40(5): 750-755.
[10]	于瑞云，薛林，安轩邈，夏兴有. 基于改进GA-BP的移动通信用户流失预测算法[J]. 东北大学学报:自然科学版, 2019, 40(2): 180-185.
[11]	刘永刚，侯立良，秦大同，胡明辉. 基于BP神经网络的煤层硬度多等级识别方法[J]. 东北大学学报:自然科学版, 2018, 39(8): 1163-1168.
[12]	王巧云，郑念祖. 基于样本选择与PSO-ANN的葡萄酒酒精浓度预测[J]. 东北大学学报:自然科学版, 2018, 39(7): 970-975.
[13]	任松，李振元，陈钒，姜德义. 石膏围岩隧道衬砌结构腐蚀模型研究[J]. 东北大学学报:自然科学版, 2017, 38(7): 1049-1054.
[14]	张钦礼，刘奇，赵建文. 全尾砂絮凝沉降参数预测模型研究[J]. 东北大学学报:自然科学版, 2016, 37(6): 875-879.
[15]	谢承煜，罗周全，贾楠，汪伟. 改进TOPSIS与GA-BP耦合的采空区危险性辨析[J]. 东北大学学报:自然科学版, 2016, 37(3): 440-445.