paa: Computes a Piecewise Aggregate Approximation (PAA) for a time...

In jmotif: Time Series Analysis Toolkit Based on Symbolic Aggregate Discretization, i.e. SAX

Description

Computes a Piecewise Aggregate Approximation (PAA) for a time series.

Usage

paa(ts, paa_num)

Arguments

ts	a timeseries to compute the PAA for.
paa_num	the desired PAA size.

References

Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S., Dimensionality reduction for fast similarity search in large time series databases. Knowledge and information Systems, 3(3), 263-286. (2001)

Examples

x = c(-1, -2, -1, 0, 2, 1, 1, 0)
x_paa3 = paa(x, 3)
#
plot(x, type = "l", main = c("8-points time series and its PAA transform into three points",
                          "PAA shown schematically in blue"))
points(x, pch = 16, lwd = 5)
#
paa_bounds = c(1, 1+7/3, 1+7/3*2, 8)
abline(v = paa_bounds, lty = 3, lwd = 2, col = "cornflowerblue")
segments(paa_bounds[1:3], x_paa3, paa_bounds[2:4], x_paa3, col = "cornflowerblue", lwd = 2)
points(x = c(1, 1+7/3, 1+7/3*2) + (7/3)/2, y = x_paa3, pch = 15, lwd = 5, col = "cornflowerblue")

Example output

Loading required package: Rcpp

jmotif documentation built on March 26, 2020, 7:23 p.m.