linearSegmentation: Piecewise linear segmentation of a time series

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Piecewise linear segmentation of a time series

Description

Locates the change-points of time series based on a piecewise linear segmentation algorithm. Given a window size (n.fit) and an angle tolerance (angle.tolerance), the segmentation algorithm starts by finding the slope of the first n.fit points of the series via least squares regression. The window is slid over one point to the right, the points within the new window are regressed, and the new slope is compared to the old slope. If the change in slope exceeds the specified angle.tolerance, a change-point is recorded as the rightmost point of the previous iteration's window. The routine then picks up again starting at the point just to the right of the change-point. If the change in slope does not exceed the specified angle.tolerance, then the old slope is updated (in a running average sense), and the algorithm continues as usual. The window is slid along the until its rightmost point reaches the edge of the time series.

Usage

linearSegmentation(x, y, n.fit=5, angle.tolerance=5,
    aspect=TRUE, plot=FALSE, add=FALSE, ...)

Arguments

x	the independent variable of the time series.
y	the dependent variable of the time series.
...	additional arguments sent unaltered to the abline function used to draw the vertical divisions of the linear segmentation. Only used if plot=TRUE.
add	a logical value. If TRUE, the plot is added using the current plotting scheme. Default: FALSE.
angle.tolerance	the maximum angle in degrees that the running average of the slopes in the current set of points can change relative to the slope of the data calculated in the most current (rightmost) window before a change-point is recorded. Default: 5.
aspect	a logical value. If TRUE, the aspect ratio of the data (defined by range(y) / range(x)) is used to adjust the angle.tolerance. Specifically, the new angle tolerance becomes angle.tolerance / aspect.ratio. Using the aspect ratio to dilate angle.tolerance allows the user to specify the degree of variability they wish to impose on the data vis-a-vis a standard plot of the data, i.e. what you would see if you issued plot(xdata, ydata). The idea is that when looking at such plots, one might decide (for example) that a 5 degree variability on the plot would be acceptable. But if that range of y is vastly different from that of x, then the true change in angle from one section to the other will be much different than 5 degrees. Thus, aspect can be used to compensate for aspect ratios far away from unity. Default: TRUE.
n.fit	an integer denoting the window size, not to exceed the number of samples in the time series. Default: 5.
plot	a logical value. If TRUE, a plot of the segmentation is displayed. Default: FALSE.

Value

a vector containing the result.

See Also

lm.

Examples

## obtain some data with approximately
## piecewise-linear trends
x <- seq(0,2*pi,length=100)
y <- sin(x)

## perform linear segmentation with aspect ratio
## dilation using a 5 degree tolerance and 5
## point windows
z <- linearSegmentation(x, y, n.fit=5, angle.tolerance=5, aspect=TRUE)

## plot the data and the estimated change-points
plot(x, y)
abline(v=x[z], lty=2)

ifultools documentation built on July 14, 2022, 5:07 p.m.