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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper examines the problem of testing whether a discrete time-seriesvector contains a periodic signal or is merely noise. To do this we examine thestochastic behaviour of the maximum intensity of the observed time-seriesvector and formulate a simple hypothesis test that rejects the null hypothesisof exchangeability if the maximum intensity spike in the Fourier domain is "toobig" relative to its null distribution. This comparison is undertaken bysimulating the null distribution of the maximum intensity using randompermutations of the time-series vector. We show that this test has a p-valuethat is uniformly distributed for an exchangeable time-series vector, and thatthe p-value increases when there is a periodic signal present in the observedvector. We compare our test to Fisher's spectrum test, which assumes normalityof the underlying noise terms. We show that our test is more robust than thistest, and accommodates noise vectors with fat tails.

The Permutation-Spectrum Test: Identifying Periodic Signals using the Maximum Fourier Intensity