Say, you have a time series object that you want to work with. You might want to check if the time series happens to be generated by white noise only, this might happen not only during the interview but also in real life sometime.

If you have three contiguous time points, assuming they are of different values. Then there are 6 permutations to order them. And four of them form a turning point (PEAK/PIT).

The expectation of number of turning points for a time series generated by white noise is then 2/3 * n, and actually they falls into a normal distribution where the variance is 8/45*n.

So your 5% confidence test should check if the number of turning points is within the range of: 2/3*n +- 1.96*sqrt(8/45*n):

require(pastecs)

data <- rnorm(10000, 0, 1)

plot(data)

limit <- function(n){

print(2/3 * n)

print(2/3 * n – 1.96 * sqrt(8/45 * n))

print(2/3 * n + 1.96 * sqrt(8/45 * n))

}

turnpoints(data)

limit(10000)

OUTPUT

> turnpoints(data)

Turning points for: data nbr observations : 10000 nbr ex-aequos : 0 nbr

turning points: 6657 (first point is a peak) E(p) = 6665.333 Var(p) = 1777.456 (theoretical)

> limit(10000) [1] 6666.667 [1] 6584.026 [1] 6749.308

You can clearly see that the fake time series does actually have 6657 turning points and it has been tested falls into our “BLACK LIST INTERVAL”!

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