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

Entropy measures have become increasingly popular as an evaluation metric forcomplexity in the analysis of time series data, especially in physiology andmedicine. Entropy measures the rate of information gain, or degree ofregularity in a time series e.g. heartbeat. Ideally, entropy should be able toquantify the complexity of any underlying structure in the series, as well asdetermine if the variation arises from a random process. Unfortunately currententropy measures mostly are unable to perform the latter differentiation. Thus,a high entropy score indicates a random or chaotic series, whereas a low scoreindicates a high degree of regularity. This leads to the observation thatcurrent entropy measures are equivalent to evaluating how random a series is,or conversely the degree of regularity in a time series. This raises thepossibility that existing tests for randomness, such as the runs test orpermutation test, may have similar utility in diagnosing certain conditions.This paper compares various tests for randomness with existing entropy-basedmeasurements such as sample entropy, permutation entropy and multi-scaleentropy. Our experimental results indicate that the test statistics of the runstest and permutation test are often highly correlated with entropy scores andmay be able to provide further information regarding the complexity of timeseries.

Comparing entropy with tests for randomness as a measure of complexity in time series