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
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Time Series Discord Discovery Based on iSAX Symbolic Representation
KSE '11 Proceedings of the 2011 Third International Conference on Knowledge and Systems Engineering
Finding time series discords based on haar transform
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Among several existing algorithms proposed to solve the problem of time series discord discovery, HOT SAX and WAT are two widely used algorithms. Especially, WAT can make use of the multi-resolution property in Haar wavelet transform. In this work, we employ state-of-the-art iSAX representation rather than SAX representation in WAT algorithm. To apply iSAX in WAT algorithm, we have to devise two new auxiliary functions and also modify iSAX index structure to adapt Haar transform that is used in WAT algorithm. We empirically evaluate our algorithm with a set of experiments. Experimental results show that WATiSAX algorithm is more effective than original WAT algorithm.