[1]陈俐名,黄诗茹,修保新,等.基于串行分类算法的不平衡时间序列多分类方法[J].南京师范大学学报(工程技术版),2019,19(03):008.[doi:10.3969/j.issn.1672-1292.2019.03.002]
 Chen Liming,Huang Shiru,Xiu Baoxin,et al.Imbalanced Time Series Multi-Classification MethodBased on Two-Steps Algorithm[J].Journal of Nanjing Normal University(Engineering and Technology),2019,19(03):008.[doi:10.3969/j.issn.1672-1292.2019.03.002]
点击复制

基于串行分类算法的不平衡时间序列多分类方法
分享到:

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
19卷
期数:
2019年03期
页码:
008
栏目:
计算机工程
出版日期:
2019-09-30

文章信息/Info

Title:
Imbalanced Time Series Multi-Classification MethodBased on Two-Steps Algorithm
文章编号:
1672-1292(2019)03-0008-07
作者:
陈俐名黄诗茹修保新周 鋆
国防科技大学信息系统工程重点实验室,湖南 长沙 410073
Author(s):
Chen LimingHuang ShiruXiu BaoxinZhou Yun
Science and Technology on Information Systems Engineering Laboratory,National University of Defense Technology,Changsha 410073,China
关键词:
不平衡时间序列多分类串行分类算法
Keywords:
imbalancetime seriesmulti-classificationtwo-steps algorithm
分类号:
TP311
DOI:
10.3969/j.issn.1672-1292.2019.03.002
文献标志码:
A
摘要:
提出了基于串行分类算法的不平衡时间序列多分类方法,并以“上证50指数”15 min交易数据为例,进行了实验检验与结果分析. 结果表明,在多数情况下,串行分类算法比单一算法有更高的准确率、召回率和F1值,可以更有效解决不平衡时间序列多分类问题.
Abstract:
This paper proposes a multi-classification method of imbalanced time series based on two-steps classification algorithm,and takes the 15-minutes trading data of“Shanghai Stock Exchange 50 index”as an example to conduct an experimental test and analysis. The results show that,in most cases,the two-steps classification algorithm has a higher accuracy,recall rate and F1 value than the single algorithm,and that it can solve the problem of multiple classification of unbalanced time series more effectively.

参考文献/References:

[1] WANG Z,YAN W,OATES T. Time series classification from scratch with deep neural networks:a strong baseline[C]//2017 International Joint Conference on. Alaska,USA:IEEE,2017.
[2]KARIM F,MAJUMDAR S,DARABI H,et al. LSTM fully convolutional networks for time series classification[J]. IEEE access,2017,6(99):1662-1669.
[3]AHN J,LEE J H. Clustering algorithm for time series with similar shapes[J]. KSII transactions on internet and information systems,2018,12(7):3112-3127.
[4]JERZAK Z,ZIEKOW H. The DEBS 2014 grand challenge[C]//ACM Press the 8th ACM International Conference. Mumbai,India,2014.
[5]MUTSCHLER C,ZIEKOW H,JERZAK Z. The DEBS 2013 grand challenge[C]//ACM Press the 7th ACM International Conference. California,USA,2013.
[6]LINES J,DAVIS L M,HILLS J,et al. A shapelet transform for time series classification[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing,China,2012.
[7]LIU X Y,REN C L. Fast subsequence matching under time warping in time-series databases[C]//2013 International Conference on Machine Learning and Cybernetics(ICMLC). Tianjin,China,2013.
[8]WEISS G M. Mining with rarity:a unifying framework[J]. ACM sigkdd explorations newsletter,2004,6(1):7-19.
[9]PURWANTO P,CHIKKANNAN E. Enhanced hybrid prediction models for time series prediction[J]. The international arab journal of information technology,2018,15(5):866-874.
[10]KEOGH E,LIN J,TRUPPEL W. Clustering of time series subsequences is meaningless:implications for previous and future research[J]. Knowledge & information systems,2005,8(2):154-177.
[11]ERGEZER H,LEBLEBICIOGLU K. Time series classification using point-wise features[C]//IEEE 2017 25th Signal Processing and Communications Applications Conference(SIU). Antalya,Turkey,2017.
[12]RAJKOMAR A,OREN E,CHEN K,et al. Scalable and accurate deep learning for electronic health records[J]. NPJ digital medicine,2018,18:1-18.
[13]NWEKE H F,TEH Y W,AL-GARADI M A,et al. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks:state of the art and research challenges[J]. Expert systems with applications,2018,105:233-261.
[14]WANG J,CHEN Y,HAO S,et al. Deep learning for sensor-based activity recognition:a survey[J]. Pattern recognition letters,2019,119:3-11.
[15]NWE T L,DAT T H,MA B. Convolutional neural network with multi-task learning scheme for acoustic scene classification[C]//9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. Kuala Lumpur,2017.
[16]SUSTO G A,CENEDESE A,TERZI M. Big data application in power systems[M]. Canada:Elseview,2018:179-220.
[17]BAGNALL A,LINES J,BOSTROM A,et al. The great time series classification bake off:a review and experimental evaluation of recent algorithmic advances[J]. Data mining and knowledge discovery,2017,31(3):606-660.
[18]YI M,CHEN W,CHEN Y,et al. An integrated data mining approach to real-time clinical monitoring and deterioration warning[C]//ACM Sigkdd International Conference on Knowledge Discovery & Data Mining. Not Wiki,2012.
[19]KRAWCZYK B,GALAR M,JELEN L,et al. Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy[J]. Applied soft computing,2016,38:714-726.
[20]WEI W,LI J,CAO L,et al. Effective detection of sophisticated online banking fraud on extremely imbalanced data[J]. World wide web,2013,16(4):449-475.
[21]MARKOWITZ H M. Portfolio selection[J]. Journal of finance,1952,7(1):77-91.
[22]周志华. 机器学习[M]. 北京:清华大学出版社,2016.
ZHOU Z H. Machine learning[M]. Beijing:Tsinghua University Press,2016.(in Chinese)
[23]KEOGH E,KASETTY S. On the need for time series data mining benchmarks:a survey and empirical demonstration[J]. Data mining and knowledge discovery,2003,7(4):349-371.
[24]王小川,陈杰,卢威等. Python与量化投资:从基础到实战[M]. 北京:电子工业出版社,2018.
WANG X C,CHEN J,LU W,et al. Python and quantitative investing:from basics to practice[M]. Beijing:Publishing House of Electronics Industry,2018.(in Chinese)
[25]蔡立耑. 量化投资:以Python为工具[M]. 北京:电子工业出版社,2017.
CAI L D.Quantitative investing:use Python as a tool[M]. Beijing:Publishing House of Electronics Industry,2017.(in Chinese)
[26]李航. 统计学习方法[M]. 北京:清华大学出版社,2012.
LI H. Statistical learning methods[M]. Beijing:Tsinghua University Press,2012.(in Chinese)

相似文献/References:

[1]张 军,陈汉武,马志民.一种时间序列相似性的快速搜索算法[J].南京师范大学学报(工程技术版),2005,05(03):050.
 ZHANG Jun,CHEN Hanwu,MA Zhimin.An Algorithm for Similar Sub-patterns Discovery From Time Series[J].Journal of Nanjing Normal University(Engineering and Technology),2005,05(03):050.

备注/Memo

备注/Memo:
收稿日期:2019-07-05.
基金项目:国家自然科学基金(61703416)、湖南省自然科学基金(2018JJ3614).
通讯联系人:修保新,博士,副研究员,研究方向:体系行为认知. E-mail:baoxinxiu@163.com
更新日期/Last Update: 2019-09-30