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

I propose a frequency domain adaptation of the Expectation Maximization (EM)algorithm to group a family of time series in classes of similar dynamicstructure. It does this by viewing the magnitude of the discrete Fouriertransform (DFT) of each signal (or power spectrum) as a probabilitydensity/mass function (pdf/pmf) on the unit circle: signals with similardynamics have similar pdfs; distinct patterns have distinct pdfs. An advantageof this approach is that it does not rely on any parametric form of the dynamicstructure, but can be used for non-parametric, robust and model-freeclassification. This new method works for non-stationary signals of similarshape as well as stationary signals with similar auto-correlation structure.Applications to neural spike sorting (non-stationary) and pattern-recognitionin socio-economic time series (stationary) demonstrate the usefulness and wideapplicability of the proposed method.

A Nonparametric Frequency Domain EM Algorithm for Time Series Classification with Applications to Spike Sorting and Macro-Economics