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

Knowing who follows whom and what patterns they are following are crucialsteps to understand collective behaviors (e.g. a group of human, a school offish, or a stock market). Time series is one of resources that can be used toget insight regarding following relations. However, the concept of followingpatterns or motifs and the solution to find them in time series are notobvious. In this work, we formalize a concept of following motifs between twotime series and present a framework to infer following patterns between twotime series. The framework utilizes one of efficient and scalable methods toretrieve motifs from time series called the Matrix Profile Method. We compareour proposed framework with several baselines. The framework performs betterthan baselines in the simulation datasets. In the dataset of sound recording,the framework is able to retrieve the following motifs within a pair of timeseries that two singers sing following each other. In the cryptocurrencydataset, the framework is capable of capturing the following motifs within apair of time series from two digital currencies, which implies that the valuesof one currency follow the values of another currency patterns. Our frameworkcan be utilized in any field of time series to get insight regarding followingpatterns between time series.

Framework for Variable-lag Motif Following Relation Inference In Time Series using Matrix Profile analysis