adore.filter: A Robust Adaptive Online Repeated Median Filter for...

In robfilter: Robust Time Series Filters

A Robust Adaptive Online Repeated Median Filter for Univariate Time Series

Description

Procedure for robust online extraction of low frequency components (the signal) from a univariate time series by a moving window technique with adaptive window width selection (ADaptive Online REpeated median FILTER).

Usage

adore.filter(y,
             p.test=15, minNonNAs=5,
             min.width=10, max.width=200,
             width.search="geometric",
             rtr=2, extrapolate=FALSE,
             calc.qn=FALSE, sign.level=0.1)

Arguments

y	a numeric vector or (univariate) time series object.
p.test	defines the number of most recent Repeated Median residuals within each window used to test the goodness of fit of the online signal level. It can be either a value in (0.25, 0.3, 0.5), meaning that floor(p.test*width) residuals are considered for the goodness of fit test, where width is the currently used window width, or it can also be a positive integer >= 5 specifying a fixed number of most recent residuals (default). If the number of residuals considered for the test exceeds width/2, the procedure sets it to floor(width/2), if it is smaller than five, the number is set to five.
minNonNAs	a positive integer >= 5 defining the minimum number of non-missing observations within one window which is required for a ‘sensible’ estimation.
min.width	a positive integer >= 5 specifying the minimal window width.
max.width	a positive integer >= min.width specifying the maximal window width.
width.search	a character string defining the search algorithm used for finding an adequate window width at each point in time. "linear" The linear search always results in the largest window width possible and hence yields the smoothest online signal. However, if sudden changes (like level shifts) appear in the signal it requires a lot of computation time and thus, an increased variability of the extracted signal may be observed. "binary" The binary search is recommended if it can be expected that the window width needs to be reduced drastically from a large to a very small value at certain times (for example at level shifts or trend changes). However, it may not always result in the largest possible window width. "geometric" (default) The geometric search is as fast as the binary search but it puts more weight on large window widths. It offers a good compromise between the linear and the binary search (computation time vs. smooth output signal).
rtr	a value in 0, 1, 2 specifying whether a 'restrict to range' rule should be applied. rtr=0 The estimated signal level consists of the last fitted value of a Repeated Median regression fit within a time window of adequate width. rtr=1 The level estimation is restricted to the range of the observations within each time window. rtr=2 (default) The level estimation is restricted to the range of the most recent observations (specified by p.test) i.e., to the range of the observations which are used to evaluate the goodness of fit.
extrapolate	a logical indicating whether the level estimations should be extrapolated to the beginning of the time series. The extrapolation consists of all fitted values within the first time window.
calc.qn	a logical indicating whether the Qn scale (Rousseeuw, Croux, 1993) should also be calculated along with the signal level as an estimate of the standard deviation in each window. Here, the Qn command from the robustbase library is applied with the built-in finite sample correction.
sign.level	significance level of the test procedure; must be a value in (0,0.5).

Details

The adore.filter works by applying Repeated Median (RM) regression (Siegel, 1982) to a moving time window with a length varying between min.width and max.width.

For each point in time, the window width is adapted to the current data situation by a goodness of fit test for the most recent signal level estimation. The test uses the absolute value of the sum of the RM residuals in the subset specified by p.test. The critical value for the test decision corresponds to a slightly modified 0.95-quantile of the distribution of the test statistic and is stored in the data set critvals.

A more detailed description of the filter can be found in Schettlinger, Fried, Gather (2010).

Value

adore.filter returns an object of class adore.filter. An object of class adore.filter is a list containing the following components:

level	a numeric vector containing the signal level extracted by the RM filter with adaptive window width.
slope	a numeric vector containing the corresponding slope within each time window.
width	a numeric vector containing the corresponding window width used for the level and slope estimations.
level.list	a list which contains with as many elements as the length of the input time series. If at time t, the window width was not reduced, the entry level.list[[t]] simply corresponds to level[t]. However, if more than one iteration took place, level.list[[t]] is a vector which contains all level estimations which were evaluated until the final estimate mu[t] passed the goodness of fit test and was stored.
slope.list	a list containing the slope estimations corresponding to the values in level.list.
width.list	a list containing the window widths used for the estimations in level.list and slope.list.
sigma	a numeric vector containing the corresponding scale within each time window estimated by the robust Qn estimator (only calculated if calc.qn = TRUE, else sigma does not exist).

In addition, the original input time series is returned as list member y, and the settings used for the analysis are returned as the list members min.width, max.width, width.search, p.test, minNonNAs, rtr, extrapolate, and calc.qn.

Application of the function plot to an object of class aoRM returns a plot showing the original time series with the filtered output.

Author(s)

Karen Schettlinger

References

Rousseeuw, P. J., Croux, C. (1993) Alternatives to the Median Absolute Deviation, Journal of the American Statistical Association 88, 1273-1283.

Schettlinger, K., Fried, R., Gather, U. (2010) Real Time Signal Processing by Adaptive Repeated Median Filters, International Journal of Adaptive Control and Signal Processing 24(5), 346-362.

Siegel, A.F. (1982) Robust Regression Using Repeated Medians, Biometrika 69 (1), 242-244.

See Also

robreg.filter, wrm.filter, madore.filter, scarm.filter.

Examples

# # # # # # # # # #
# Short and noise-free time series
series <- c(rep(0,30),rep(10,30),seq(10,5,length=20),seq(5,15,length=20))

# Adaptive online signal extraction without & with 'restrict to range' rule
t.without.rtr <- adore.filter(series, rtr=0)
plot(t.without.rtr)
t.with.rtr1 <- adore.filter(series, rtr=1)
lines(t.with.rtr1$level, col="blue")
t.with.rtr2 <- adore.filter(series)
lines(t.with.rtr2$level, col="green3",lty=2)
legend("top",c("Signal with rtr=1","Signal with rtr=2"),col=c("blue","green3"),lty=c(1,2),bty="n")

# # # # # # # # # #
# Short and noise-free time series + 1 outlier
ol.series <- series
ol.series[63] <- 3

# Adaptive online signal extraction without & with 'restrict to range' rule
t.without.rtr <- adore.filter(ol.series, rtr=0)
plot(t.without.rtr)
t.with.rtr1 <- adore.filter(ol.series, rtr=1)
lines(t.with.rtr1$level, col="blue")
t.with.rtr2 <- adore.filter(ol.series)
lines(t.with.rtr2$level, col="green3",lty=2)
legend("top",c("Signal with rtr=1","Signal with rtr=2"),col=c("blue","green3"),lty=c(1,2),bty="n")

# # # # # # # # # #
# Noisy time series with level shifts, trend changes and shifts in the scale of the error term
true.signal  <- c(rep(0,150),rep(10,150),seq(10,5,length=100),seq(5,15,length=100))
series2      <- true.signal + c(rnorm(250,sd=1), rnorm(200,sd=3), rnorm(50,sd=1))

# Adaptive online signal extraction with additional Qn scale estimation
s2 <- adore.filter(series2, calc.qn=TRUE)
par(mfrow=c(3,1))
plot(s2)
plot(s2$sigma,type="l",main="Corresponding Qn Scale Estimation",ylab="sigma",xlab="time")
lines(c(rep(1,250),rep(3,200),rep(1,150)),col="grey")
legend("topleft",c("True scale","Qn"),lty=c(1,1),col=c("grey","black"),bty="n")
plot(s2$width,type="l",main="Corresponding Window Width",ylab="width",xlab="time")

robfilter documentation built on Sept. 11, 2024, 6:05 p.m.