Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Outlier Mining in Large High-Dimensional Data Sets
IEEE Transactions on Knowledge and Data Engineering
Outlier detection using default reasoning
Artificial Intelligence
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
Arrhythmia classification using local hölder exponents and support vector machine
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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We present a method of detecting and localising outliers in stochastic processes. The method checks the internal consistency of the scaling behaviour of the process within the paradigm of the multifractal spectrum. Deviation from the expected spectrum is interpreted as the potential presence of outliers. The detection part of the method is then supplemented by the localisation analysis part, using the local scaling properties of the time series. Localised outliers can then be removed one by one, with the possibility of dynamic verification of spectral properties. Both the multifractal spectrum formalism and the local scaling properties of the time series are implemented on the wavelet transform modulus maxima tree.