English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

WAT: Finding Top-K Discords in Time Series Database