Publications (4)0.99 Total impact
Article: Erratum to:Journal of Computational and Applied Mathematics 09/2009; · 0.99 Impact Factor
- J. Computational Applied Mathematics. 01/2009; 231:1004.
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ABSTRACT: 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.Journal of Computational and Applied Mathematics. 01/2008;
Conference Proceeding: Entropy-Based Symbolic Representation for Time Series Classification[show abstract] [hide abstract]
ABSTRACT: In order to improve the performance of time-series classification, we introduce a new approach of time series classification. The first basic idea of the approach is to use entropy impurity measure to discretize and symbolize time series, which discretize the original time series into disjoint intervals using entropy impurity measure and then transform the time series into symbolic representations. The second idea of the approach is to combine symbolic representation of time series and k nearest neighbor to classify time series. The proposed approach is compared with a number of known pattern classifiers by benchmarking with the use of artificial and real-world data sets. The experimental results show it can reduce the error rates of time series classification, so it is highly competitive with previous approaches.Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on; 09/2007