Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 482-487.

• Big Data & Data Mining • Previous Articles     Next Articles

Boundary Distance Algorithm for Determining Sliding Window Size

PENG Cheng1,2, HE Jing1, CHI Hao1   

  1. School of Computer,Hunan University of Technology,Zhuzhou,Hunan 412007,China1;
    School of Automation,Central South University,Changsha 410083,China2
  • Online:2019-06-14 Published:2019-07-02

Abstract: Due to a large amount of information and high density of the original measurement data collected by most equipment,the existing time series sliding window dimension reduction method uses the empirical value to determine the window size,which cannot retain important information points of the data to the utmost extent,and has high computational complexity.To this end,the influence of sliding window on time series similarity technology in practical applications was discussed,and an algorithm for determining the initial scale of sliding window was proposed.The upper and lower boundary curves with higher fitting degree are constructed,and the trend weighting is introduced into the LB_Hust distance calculation method,which reduces the difficulty of mathematical modeling and improves efficiency of equipment data similarity classification and state evaluation.

Key words: Data mining, LB_Hust distance, Sliding window

CLC Number: 

  • TP301
[1]CHERNICK M R.Wavelet Methods for Time Series Analysis[J].Technometrics,2016,43(4):491-497.
[2]ANDREW B,KELVYN J.Explaining Fixed Effects:Random Effects Modeling of Time-Series Cross-Sectional and Panel Data*[J].Political Science Research & Methods,2015,3(1):133-153.
[3]BULLMORE E,LONG C,SUCKLING J,et al.Colored noise and computational inference in neurophysiological (fMRI) time series analysis:Resampling methods in time and wavelet domains[J].Human Brain Mapping,2015,12(2):61-78.
[5]ADWAN S,ALSALEH I,MAJED R.A new approach for image stitching technique using Dynamic Time Warping (DTW) algorithm towards scoliosisX-ray diagnosis[J].Measurement,2016,84:32-46.
[6]CHEN T L,CHEN F Y.An intelligent pattern recognition mo-del for supporting investment decisions in stock market[J].Information Sciences,2016,346:261-274.
[8]XIAO J,BAI L,LI F,et al.Sizing of Energy Storage and Diesel Generators in an Isolated Microgrid Using Discrete Fourier Transform (DFT)[J].IEEE Transactions on Sustainable Energy,2014,5(3):907-916.
[10]HU B,DIXON P C,JACOBS J V,et al.Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking[J].Journal of Biomechanics,2018,71:37-42.
[11]YAO R,LIN G S,SHI Q F,et al.Efficient Dense Labelling of Human Activity Sequences from Wearables using Fully Convolutional Networks[J].Pattern Recognition,2017,78:252-266.
[15]LEE G,YUN U,RYU K H.Sliding window based weighted maximal frequent pattern mining over data streams[J].Expert Systems with Applications,2014,41(2):694-708.
[18]BIAN W,TAO D.Max-Min Distance Analysis by Using Se-quential SDP Relaxation for Dimension Reduction[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2011,33(5):1037-1050.
[19]NIENNATTRAKUL V,RUENGRONGHIRUNYA P,RA-TANAMAHATANA C A.Exact indexing for massive time series databases under time warping distance[J].Data Mining & Knowledge Discovery,2010,21(3):509-541.
[20]KEOGH E,RATANAMAHATANA C A.Exact indexing of dynamic time warping[J].Knowledge & Information Systems,2005,7(3):358-386.
[21]KDD’ Datasets.The UCI KDD Archive[Z].1999.
[22]KOU G,PENG Y,WANG G.Evaluation of clustering algo-rithms for financial risk analysis using MCDM methods[J].Information Sciences,2014,275(11):1-12.
[1] LI Rong-fan, ZHONG Ting, WU Jin, ZHOU Fan, KUANG Ping. Spatio-Temporal Attention-based Kriging for Land Deformation Data Interpolation [J]. Computer Science, 2022, 49(8): 33-39.
[2] YAO Xiao-ming, DING Shi-chang, ZHAO Tao, HUANG Hong, LUO Jar-der, FU Xiao-ming. Big Data-driven Based Socioeconomic Status Analysis:A Survey [J]. Computer Science, 2022, 49(4): 80-87.
[3] KONG Yu-ting, TAN Fu-xiang, ZHAO Xin, ZHANG Zheng-hang, BAI Lu, QIAN Yu-rong. Review of K-means Algorithm Optimization Based on Differential Privacy [J]. Computer Science, 2022, 49(2): 162-173.
[4] MA Dong, LI Xin-yuan, CHEN Hong-mei, XIAO Qing. Mining Spatial co-location Patterns with Star High Influence [J]. Computer Science, 2022, 49(1): 166-174.
[5] ZHANG Ya-di, SUN Yue, LIU Feng, ZHU Er-zhou. Study on Density Parameter and Center-Replacement Combined K-means and New Clustering Validity Index [J]. Computer Science, 2022, 49(1): 121-132.
[6] GONG Jian-feng. Resisting Power Analysis Algorithm of Scalar Multiplication Based on Signed Sliding Window [J]. Computer Science, 2021, 48(6A): 533-537.
[7] XU Hui-hui, YAN Hua. Relative Risk Degree Based Risk Factor Analysis Algorithm for Congenital Heart Disease in Children [J]. Computer Science, 2021, 48(6): 210-214.
[8] ZHANG Yan-jin, BAI Liang. Fast Symbolic Data Clustering Algorithm Based on Symbolic Relation Graph [J]. Computer Science, 2021, 48(4): 111-116.
[9] ZHANG Han-shuo, YANG Dong-ju. Technology Data Analysis Algorithm Based on Relational Graph [J]. Computer Science, 2021, 48(3): 174-179.
[10] ZOU Cheng-ming, CHEN De. Unsupervised Anomaly Detection Method for High-dimensional Big Data Analysis [J]. Computer Science, 2021, 48(2): 121-127.
[11] LIU Xin-bin, WANG Li-zhen, ZHOU Li-hua. MLCPM-UC:A Multi-level Co-location Pattern Mining Algorithm Based on Uniform Coefficient of Pattern Instance Distribution [J]. Computer Science, 2021, 48(11): 208-218.
[12] LIU Xiao-nan, SONG Hui-chao, WANG Hong, JIANG Duo, AN Jia-le. Survey on Improvement and Application of Grover Algorithm [J]. Computer Science, 2021, 48(10): 315-323.
[13] ZHANG Yu, LU Yi-hong, HUANG De-cai. Weighted Hesitant Fuzzy Clustering Based on Density Peaks [J]. Computer Science, 2021, 48(1): 145-151.
[14] YOU Lan, HAN Xue-wei, HE Zheng-wei, XIAO Si-yu, HE Du, PAN Xiao-meng. Improved Sequence-to-Sequence Model for Short-term Vessel Trajectory Prediction Using AIS Data Streams [J]. Computer Science, 2020, 47(9): 169-174.
[15] ZHANG Su-mei and ZHANG Bo-tao. Evaluation Model Construction Method Based on Quantum Dissipative Particle Swarm Optimization [J]. Computer Science, 2020, 47(6A): 84-88.
Full text



No Suggested Reading articles found!