WAT: Finding top-K discords in time series database

Abstract

Finding discords in time series database is an important problem in a great variety of applications, such as space shuttle telemetry, mechanical industry, biomedicine, and financial data analysis. However, most previous methods for this problem suffer from too many parameter settings which are difficult for users. The best known approach to our knowledge that has comparatively fewer parameters still requires users to choose a word size for the compression of subsequences. In this paper, we propose a Haar wavelet and augmented trie based algorithm to mine the top-K discords from a time series database, which can dynamically determine the word size for compression. Due to the characteristics of Haar wavelet transform, our algorithm has greater pruning power than previous approaches. Through experiments with some annotated datasets, the effectiveness and efficiency of our algorithm are both attested.