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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Anomaly detection for time-series data has been an important research fieldfor a long time. Seminal work on anomaly detection methods has been focussingon statistical approaches. In recent years an increasing number of machinelearning algorithms have been developed to detect anomalies on time-series.Subsequently, researchers tried to improve these techniques using (deep) neuralnetworks. In the light of the increasing number of anomaly detection methods,the body of research lacks a broad comparative evaluation of statistical,machine learning and deep learning methods. This paper studies 20 univariateanomaly detection methods from the all three categories. The evaluation isconducted on publicly available datasets, which serve as benchmarks fortime-series anomaly detection. By analyzing the accuracy of each method as wellas the computation time of the algorithms, we provide a thorough insight aboutthe performance of these anomaly detection approaches, alongside some generalnotion of which method is suited for a certain type of data.

Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art