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/

Zendy Plus

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

Zendy Tools

Zendy Open

We develop an anomaly-detection method when systematic anomalies, possiblystatistically very similar to genuine inputs, are affecting control systems atthe input and/or output stages. The method allows anomaly-free inputs (i.e.,those before contamination) to originate from a wide class of random sequences,thus opening up possibilities for diverse applications. To illustrate how themethod works on data, and how to interpret its results and make decisions, weanalyze several actual time series, which are originally non-stationary but inthe process of analysis are converted into stationary. As a furtherillustration, we provide a controlled experiment with anomaly-free inputsfollowing an ARMA time series model under various contamination scenarios.

Detecting systematic anomalies affecting systems when inputs are stationary time series