[1] |
PAWAR A D, KALAVADEKAR P N, TAMBE S N. A survey on outlier detection techniques for credit card fraud detection [J]. IOSR Journal of Computer Engineering, 2014, 16(2): 44-48.
|
[2] |
GOLMOHAMMADI K, ZAIANE O R. Time series contextual anomaly detection for detecting market manipulation in stock market[C]// IEEE International Conference on Data Science and Advanced Analytics. Pairs, France: IEEE Press, 2015: 1-10.
|
[3] |
KIM G, LEE S, KIM S. A novel hybrid intrusion detection method integrating anomaly detection with misuse detection[J]. Expert Systems with Applications, 2014, 41(4): 1690-1700.
|
[4] |
SCHIFF G D, VOLK L A, VOLODARSKAYA M, et al. Screening for medication errors using an outlier detection system[J]. Journal of the American Medical Informatics Association, 2017, 24(2): 281-287.
|
[5] |
BILLOR N, HADI A S, VELLEMAN P F. BACON: Blocked adaptive computationally efficient outlier nominators[J]. Computational Statistics & Data Analysis, 2000, 34(3):279-298.
|
[6] |
KNORR E M,NG R T. Algorithms for mining distance-based outliers in large datasets[C]//Proceedings of the 24th International Conference on Very Large Data Bases. San Francisco:Morgan Kaufmann Publishers,1998: 392-403.
|
[7] |
RAMASWAMY S, RASTOGI R, SHIM K. Efficient algorithms for mining outliers from large data sets[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data. Dallas, USA: ACM Press, 2000, 29(2): 427-438.
|
[8] |
BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: Identifying density-based local outliers[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data. Dallas, USA: ACM Press, 2000, 29(2): 93-104.
|
[9] |
HAWKINS D M. Identification of Outliers[M]. London: Chapman and Hall, 1980.
|
[10] |
ABRAHAM B, BOX G E P. Bayesian Analysis of Some Outlier Problems in Time Series[J]. Biometrika, 1979, 66(2):229-236.
|
[11] |
KARIMIAN S H, KELARESTAGHI M, HASHEMI S. I-IncLOF: Improved incremental local outlier detection for data streams[C]// Proceedings of the 16th CSI International Symposium on Artificial Intelligence and Signal Processing. Shiraz, Fars, Iran: IEEE Press, 2012: 23-28.
|
[12] |
潘渊洋, 李光辉, 徐勇军. 基于DBSCAN的环境传感器网络异常数据检测方法[J]. 计算机应用与软件, 2012(11): 69-72.
|
[13] |
HILL D J, MINSKER B S, AMIR E. Real-time Bayesian anomaly detection for environmental sensor data[C]// Proceedings of the 32nd Congress-International Association for Hydraulic Research. 2007, (2): 503.
|
[14] |
ERFANI S M, RAJASEGARAR S, KARUNASEKERA S, et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J]. Pattern Recognition, 2016, 58(C): 121-134.
|
[15] |
JADIDI Z, MUTHUKKUMARASAMY V, SITHIRASENAN E, et al. Flow-based anomaly detection using neural network optimized with GSA algorithm[C]// Proceedings of the 33rd International Conference on Distributed Computing Systems Workshops. Philadelphia, USA: IEEE Press, 2013: 76-81.
|
[16] |
MARTINS H, PALMA L, CARDOSO A, et al. A support vector machine based technique for online detection of outliers in transient time series[C]// 10th Asian Control Conference. Kota, Kinabalu: IEEE Press, 2015: 1-6.
|
[17] |
JOHANSEN S, NIELSEN B. Asymptotic theory of outlier detection algorithms for linear time series regression models[J]. Scandinavian Journal of Statistics, 2016, 43(2): 321-348.
|
[18] |
刘芳, 毛志忠. 过程控制时间序列中异常值的动态检测[J]. 控制理论与应用, 2012, 29(4): 424-432.
|
[19] |
杨志勇, 朱跃龙, 万定生. 基于知识粒度的时间序列异常检测研究[J]. 计算机技术与发展, 2016, 26(7): 51-54.
|
[20] |
BOX G E P, JENKINS G M, REINSEL G C, et al. Time Series Analysis: Forecasting and Control[M]. John Wiley & Sons, 2015.
|
[21] |
LACOUR C, MASSART P, RIVOIRARD V. Estimator selection: A new method with applications to kernel density estimation[J]. arXiv preprint, 2016, arXiv:1607.05091.
|
[22] |
ANDERSSON B, DAVIER A A. Improving the bandwidth selection in kernel equating[J]. Journal of Educational Measurement, 2014, 51(3): 223-238.
|
[23] |
苏卫星, 朱云龙, 胡琨元,等. 基于模型的过程工业时间序列异常值检测方法[J]. 仪器仪表学报, 2012, 33(9): 2080-2087.
|
[24] |
GUIDO D, TEUVO K.Visual Explorations in Finance: With Self-Organizing Maps[M]. Springer Science & Business Media, 2013.
|
[25] |
TAKEUCHI J I, YAMANISHI K. A unifying framework for detecting outliers and change points from time series[J]. Journal of Taiyuan Normal University, 2006, 18(4): 482-492.
|
[1] |
PAWAR A D, KALAVADEKAR P N, TAMBE S N. A survey on outlier detection techniques for credit card fraud detection [J]. IOSR Journal of Computer Engineering, 2014, 16(2): 44-48.
|
[2] |
GOLMOHAMMADI K, ZAIANE O R. Time series contextual anomaly detection for detecting market manipulation in stock market[C]// IEEE International Conference on Data Science and Advanced Analytics. Pairs, France: IEEE Press, 2015: 1-10.
|
[3] |
KIM G, LEE S, KIM S. A novel hybrid intrusion detection method integrating anomaly detection with misuse detection[J]. Expert Systems with Applications, 2014, 41(4): 1690-1700.
|
[4] |
SCHIFF G D, VOLK L A, VOLODARSKAYA M, et al. Screening for medication errors using an outlier detection system[J]. Journal of the American Medical Informatics Association, 2017, 24(2): 281-287.
|
[5] |
BILLOR N, HADI A S, VELLEMAN P F. BACON: Blocked adaptive computationally efficient outlier nominators[J]. Computational Statistics & Data Analysis, 2000, 34(3):279-298.
|
[6] |
KNORR E M,NG R T. Algorithms for mining distance-based outliers in large datasets[C]//Proceedings of the 24th International Conference on Very Large Data Bases. San Francisco:Morgan Kaufmann Publishers,1998: 392-403.
|
[7] |
RAMASWAMY S, RASTOGI R, SHIM K. Efficient algorithms for mining outliers from large data sets[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data. Dallas, USA: ACM Press, 2000, 29(2): 427-438.
|
[8] |
BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: Identifying density-based local outliers[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data. Dallas, USA: ACM Press, 2000, 29(2): 93-104.
|
[9] |
HAWKINS D M. Identification of Outliers[M]. London: Chapman and Hall, 1980.
|
[10] |
ABRAHAM B, BOX G E P. Bayesian Analysis of Some Outlier Problems in Time Series[J]. Biometrika, 1979, 66(2):229-236.
|
[11] |
KARIMIAN S H, KELARESTAGHI M, HASHEMI S. I-IncLOF: Improved incremental local outlier detection for data streams[C]// Proceedings of the 16th CSI International Symposium on Artificial Intelligence and Signal Processing. Shiraz, Fars, Iran: IEEE Press, 2012: 23-28.
|
[12] |
潘渊洋, 李光辉, 徐勇军. 基于DBSCAN的环境传感器网络异常数据检测方法[J]. 计算机应用与软件, 2012(11): 69-72.
|
[13] |
HILL D J, MINSKER B S, AMIR E. Real-time Bayesian anomaly detection for environmental sensor data[C]// Proceedings of the 32nd Congress-International Association for Hydraulic Research. 2007, (2): 503.
|
[14] |
ERFANI S M, RAJASEGARAR S, KARUNASEKERA S, et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J]. Pattern Recognition, 2016, 58(C): 121-134.
|
[15] |
JADIDI Z, MUTHUKKUMARASAMY V, SITHIRASENAN E, et al. Flow-based anomaly detection using neural network optimized with GSA algorithm[C]// Proceedings of the 33rd International Conference on Distributed Computing Systems Workshops. Philadelphia, USA: IEEE Press, 2013: 76-81.
|
[16] |
MARTINS H, PALMA L, CARDOSO A, et al. A support vector machine based technique for online detection of outliers in transient time series[C]// 10th Asian Control Conference. Kota, Kinabalu: IEEE Press, 2015: 1-6.
|
[17] |
JOHANSEN S, NIELSEN B. Asymptotic theory of outlier detection algorithms for linear time series regression models[J]. Scandinavian Journal of Statistics, 2016, 43(2): 321-348.
|
[18] |
刘芳, 毛志忠. 过程控制时间序列中异常值的动态检测[J]. 控制理论与应用, 2012, 29(4): 424-432.
|
[19] |
杨志勇, 朱跃龙, 万定生. 基于知识粒度的时间序列异常检测研究[J]. 计算机技术与发展, 2016, 26(7): 51-54.
|
[20] |
BOX G E P, JENKINS G M, REINSEL G C, et al. Time Series Analysis: Forecasting and Control[M]. John Wiley & Sons, 2015.
|
[21] |
LACOUR C, MASSART P, RIVOIRARD V. Estimator selection: A new method with applications to kernel density estimation[J]. arXiv preprint, 2016, arXiv:1607.05091.
|
[22] |
ANDERSSON B, DAVIER A A. Improving the bandwidth selection in kernel equating[J]. Journal of Educational Measurement, 2014, 51(3): 223-238.
|
[23] |
苏卫星, 朱云龙, 胡琨元,等. 基于模型的过程工业时间序列异常值检测方法[J]. 仪器仪表学报, 2012, 33(9): 2080-2087.
|
[24] |
GUIDO D, TEUVO K.Visual Explorations in Finance: With Self-Organizing Maps[M]. Springer Science & Business Media, 2013.
|
[25] |
TAKEUCHI J I, YAMANISHI K. A unifying framework for detecting outliers and change points from time series[J]. Journal of Taiyuan Normal University, 2006, 18(4): 482-492.
|