Computer Science ›› 2019, Vol. 46 ›› Issue (6): 35-40.doi: 10.11896/j.issn.1002-137X.2019.06.004
Previous Articles Next Articles
SONG Xiao-xiang, GUO Yan, LI Ning, WANG Meng
CLC Number:
| [1]SHI W,ZHU Y,ZHANG J,et al. Improving Power Grid Monitoring Data Quality:An Efficient Machine Learning Framework for Missing Data Prediction [C]∥IEEE International Con-ference on High Performance Computing and Communications.IEEE,2015:417-422. [2]BATINI C,CAPPIELLO C,FRANCALANCI C,et al. Methodo-logies for data quality assessment and improvement [J].Acm Computing Surveys,2009,41(3):1-52. [3]LUEBBERS D,GRIMMER U,JARKE M.Systematic Development of Data Mining-Based Data Quality Tools[C]∥Procee-dings of the 29th VLDB Conference.Morgan Kaufmann:San Francisco,2003:548-559. [4]WU S F,CHANG C Y,LEE S J.Time series forecasting with missing values[C]∥2015 1st International Conference on Industrial Networks and Intelligent Systems (INISCom).2015:151-156. [5]BALOUJI E,SALOR Q,ERMIS M.Exponential smoothing of multiple reference frame components with GPUs for real-time detection of time-varying harmonics and interharmonics of EAF currents [C]∥IEEE Industry Applications Society Meeting.IEEE,2017:1-8. [6]KOZERA R,WILKOLAZKA M.Natural spline interpolation and exponential parameterization for length estimation of curves [C]∥International Conference of Numerical Analysis & Applied Mathematics.AIP Publishing LLC,2017:1-140. [7]JUNNINEN H,NISKA H,TUPPURAINEN K,et al.Methods for imputation of missing values in air quality data sets[J].Atmospheric Environment,2004,38(18):2895-2907. [8]HONG S T,CHANG J W.A New Data Filtering Scheme Based on Statistical Data Analysis for Monitoring Systems in Wireless Sensor Networks[C]∥IEEE International Conference on High Performance Computing and Communications.IEEE,2011:635-640. [9]FUNG D S.Methods for the estimation of missing values in time series[J／OL].Theses Doctoratos & Masters,2006.http://ro.ecu.edu.au/theses/63. [10]LAO W,WANG Y,PENG C,et al.Time series forecasting via weighted combination of trend and seasonality respectively with linearly declining increments and multiple sine functions[C]∥2014 International Joint Conference on Neural Networks (IJCNN).2014:832-837. [11]NEWSHAM G R,BIRT B J.Building-level occupancy data to improve arima-based electricity use forecasts[C]∥Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building.ACM,New York,USA,2010:13-18. [12]SHI W,ZHU Y,ZHANG J,et al.Improving power grid monitoring data quality:An efficient machine learning framework for missing data prediction[C]∥2015 IEEE 17th International Conference on High Performance Computing and Communications.IEEE,2015:417-422. [13]WEI G,KUN N,MAN C,et al.A data prediction algorithm based on BP neural network in telecom industry[C]∥2011 International Conference on Computer Science and Service System (CSSS).2011. [14]LI L,LI Y,LI Z.Efficient missing data imputing for traffic flow by considering temporal and spatial dependence [J].Transportation Research Part C,2013,34(9):108-120. [15]QU L,LI L,ZHANG Y,et al.PPCA-based missing data imputation for traffic flow volume:a systematical approach[J].IEEE Transactions on Intelligent Transportation Systems,2009,10(3):512-522. [16]SHI W,ZHU Y,YU P,et al.Effective Prediction of Missing Data on Apache Spark over Multivariable Time Series[J].IEEE Transactions on Big Data,2017,PP(99):1. [17]CAI Y,TONG H,FAN W,et al. Fast mining of a network of coevolving time series[C]∥The 2015 SIAM International Conference on Data Mining.2015:298-306. [18]FONOLLOSA J,SHEIK S,HUERTA R,et al. Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring[J].Sensors & Actuators,2015,215:618-629. [19]RHEE I,SHIN M.Mobility traces[OL].http://carwdad.org/ncsu/mobilitymodels. [20]WU X,LIU M.In-situ soil moisture sensing:Measurement scheduling and estimation using compressive sensing [C]∥Proceedings of the 11th ACM International Conference on Information Processing in Sensor Networks.IEEE,2012:1-12. [21]CHEN S S,DONOHO D L,SAUNDERS M A.Atomic decomposition by basis pursuit[J].SIAM Review,2001,43(1):129-159. [22]TROPP J A,GILBERT A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEETransactions Information Theory,2007,53(12):4655-4666. [23]ZHANG Z,RAO B D.Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning [J].IEEE Journal of Selected Topics in Signal Processing,2011,5(5):912-926. [24]Al-SHOUKAIRI M,SCHNITER P,RAO B D.A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm [J].IEEE Transactions on Signal Processing,2018,66(2):294-308. |
| [1] | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang. Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features [J]. Computer Science, 2022, 49(7): 40-49. |
| [2] | LIU Bao-bao, YANG Jing-jing, TAO Lu, WANG He-ying. Study on Prediction of Educational Statistical Data Based on DE-LSTM Model [J]. Computer Science, 2022, 49(6A): 261-266. |
| [3] | CAI Xin-yu, FENG Xiang, YU Hui-qun. Adaptive Weight Based Broad Learning Algorithm for Cascaded Enhanced Nodes [J]. Computer Science, 2022, 49(6): 134-141. |
| [4] | SHEN Shao-peng, MA Hong-jiang, ZHANG Zhi-heng, ZHOU Xiang-bing, ZHU Chun-man, WEN Zuo-cheng. Three-way Drift Detection for State Transition Pattern on Multivariate Time Series [J]. Computer Science, 2022, 49(4): 144-151. |
| [5] | GAO Yan-lu, XU Yuan, ZHU Qun-xiong. Predicting Electric Energy Consumption Using Sandwich Structure of Attention in Double -LSTM [J]. Computer Science, 2022, 49(3): 269-275. |
| [6] | HUANG Ming, SUN Lin-fu, REN Chun-hua , WU Qi-shi. Improved KNN Time Series Analysis Method [J]. Computer Science, 2021, 48(6): 71-78. |
| [7] | ZHANG Zheng-wan, WU Di, ZHANG Chun-jiong. Study of Cellular Traffic Prediction Based on Multi-channel Sparse LSTM [J]. Computer Science, 2021, 48(6): 296-300. |
| [8] | LI Pei-guan, YU Zhi-yong, HUANG Fang-wan. Power Load Data Completion Based on Sparse Representation [J]. Computer Science, 2021, 48(2): 128-133. |
| [9] | LI Ai-ling, ZHANG Feng-li, GAO Qiang, WANG Rui-jin. Trajectory Next Footprint Prediction Model Based on Adaptive Timestamp and Multi-scale Feature Extraction [J]. Computer Science, 2021, 48(11A): 191-197. |
| [10] | WANG Xiao-di, LIU Xin, YU Xiao. Adaptive Frequency Domain Model for Multivariate Time Series Forecasting [J]. Computer Science, 2021, 48(11A): 204-210. |
| [11] | WANG Xin-ping, XIA Chun-ming, YAN Jian-jun. Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network [J]. Computer Science, 2021, 48(11): 242-249. |
| [12] | CUI Tong-tong, WANG Gui-ling, GAO Jing. Ship Trajectory Classification Method Based on 1DCNN-LSTM [J]. Computer Science, 2020, 47(9): 175-184. |
| [13] | ZHANG Wang-ce, FAN Jing, WANG Bo-ru and NI Min. (α,k)-anonymized Model for Missing Data [J]. Computer Science, 2020, 47(6A): 395-399. |
| [14] | TIAN Wei, LIU Hao, CHEN Gen-long, GONG Xiao-hui. Cross Subset-guided Adaptive Measurement for Block Compressive Sensing [J]. Computer Science, 2020, 47(12): 190-196. |
| [15] | DING Wu, MA Yuan, DU Shi-lei, LI Hai-chen, DING Gong-bo, WANG Chao. Mining Trend Similarity of Multivariate Hydrological Time Series Based on XGBoost Algorithm [J]. Computer Science, 2020, 47(11A): 459-463. |
|
||
Home