estimate.period: Estimating the Period of Noisy Periodical Data

In CircularSilhouette: Fast Silhouette on Circular or Linear Data Clusters

Estimating the Period of Noisy Periodical Data

Description

By performing circular clustering and calculating circular silhouette, the function estimates the period of periodical data.

Usage

estimate.period(x, possible.periods = diff(range(x))/2^(1:5), ks = 2:10)

Arguments

x	a numeric vector of data points that are one-dimensional, noisy, periodical
possible.periods	a numeric vector representing a set of period values to evaluate
ks	a numeric vector of numbers of clusters within one period

Details

The user can estimate a period by providing the number of clusters within one period and a set of periods for examination. An optimal circular clustering algorithm CirClust in R package OptCirClust is used to cluster the periodical data. The algorithm converts the periodical data to circular data of a circumference equal to twice the tested period. Then circular silhouette information for each circumference and number of clusters are computed to find the maximum silhouette information. The half of circumference giving maximum silhouette information is selected to be the estimated period.

The possible periods provided by the function should be close to the true period. This is not ideal and we are improving the design to be more robust.

Value

The function returns a numeric value representing the estimated period.

Examples

library(OptCirClust)
x=c(40,41,42,50,51,52,60,61,62,70,71,72,80,81,82,90,91,92)
x <- x + rnorm(length(x))
clusterrange=c(2:5)
periodrange=c(80:120)/10
period<-estimate.period(x, periodrange, clusterrange)
cat("The estimated period is", period, "\n")
plot(x, rep(1, length(x)), type="h", col="purple",
     ylab="", xlab="Noisy periodic data",
     main="Period estimation",
     sub=paste("Estimated period =", period))
k <- (max(x) - min(x)) %/% period
abline(v=min(x)+period/2 + period * (0:k), lty="dashed", col="green3")

CircularSilhouette documentation built on April 27, 2022, 9:05 a.m.