Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Two-phase clustering process for outliers detection
Pattern Recognition Letters
Mining Deviants in a Time Series Database
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Detection of unique temporal segments by information theoretic meta-clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
| Hi-index | 7.29 |
In this paper, we propose two anomaly detection algorithms PAV and MPAV on time series. The first basic idea of this paper defines that the anomaly pattern is the most infrequent time series pattern, which is the lowest support pattern. The second basic idea of this paper is that PAV detects directly anomalies in the original time series, and MPAV algorithm extraction anomaly in the wavelet approximation coefficient of the time series. For complexity analyses, as the wavelet transform have the functions to compress data, filter noise, and maintain the basic form of time series, the MPAV algorithm, while maintaining the accuracy of the algorithm improves the efficiency. As PAV and MPAV algorithms are simple and easy to realize without training, this proposed multi-scale anomaly detection algorithm based on infrequent pattern of time series can therefore be proved to be very useful for computer science applications.