The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Type: Package
Title: Fast Computation of Running Statistics for Time Series
Version: 1.1.0
Description: Provides methods for fast computation of running sample statistics for time series. These include: (1) mean, (2) standard deviation, and (3) variance over a fixed-length window of time-series, (4) correlation, (5) covariance, and (6) Euclidean distance (L2 norm) between short-time pattern and time-series. Implemented methods utilize Convolution Theorem to compute convolutions via Fast Fourier Transform (FFT).
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
URL: https://github.com/martakarass/runstats
BugReports: https://github.com/martakarass/runstats/issues
Imports: fftwtools
Suggests: covr, testthat, ggplot2, knitr, rmarkdown, sessioninfo, rbenchmark, cowplot, spelling
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2019-11-14 19:59:37 UTC; martakaras
Author: Marta Karas ORCID iD [aut, cre], Jacek Urbanek ORCID iD [aut], John Muschelli ORCID iD [ctb], Lacey Etzkorn [ctb]
Maintainer: Marta Karas <marta.karass@gmail.com>
Repository: CRAN
Date/Publication: 2019-11-14 20:30:02 UTC

Fast Running Correlation Computation

Description

Computes running correlation between time-series x and short-time pattern y.

Usage

RunningCor(x, y, circular = FALSE)

Arguments

x

A numeric vector.

y

A numeric vector, of equal or shorter length than x.

circular

logical; whether running correlation is computed assuming circular nature of x time-series (see Details).

Details

Computes running correlation between time-series x and short-time pattern y. The length of output vector equals the length of x. Parameter circular determines whether x time-series is assumed to have a circular nature. Assume l_x is the length of time-series x, l_y is the length of short-time pattern y.

If circular equals TRUE then

If circular equals FALSE then

See runstats.demo(func.name = "RunningCor") for a detailed presentation.

Value

A numeric vector.

Examples

x <- sin(seq(0, 1, length.out = 1000) * 2 * pi * 6)
y <- x[1:100]
out1 <- RunningCor(x, y, circular = TRUE)
out2 <- RunningCor(x, y, circular = FALSE)
plot(out1, type = "l"); points(out2, col = "red")


Fast Running Covariance Computation

Description

Computes running covariance between time-series x and short-time pattern y.

Usage

RunningCov(x, y, circular = FALSE)

Arguments

x

A numeric vector.

y

A numeric vector, of equal or shorter length than x.

circular

Logical; whether running variance is computed assuming circular nature of x time-series (see Details).

Details

Computes running covariance between time-series x and short-time pattern y.

The length of output vector equals the length of x. Parameter circular determines whether x time-series is assumed to have a circular nature. Assume l_x is the length of time-series x, l_y is the length of short-time pattern y.

If circular equals TRUE then

If circular equals FALSE then

See runstats.demo(func.name = "RunningCov") for a detailed presentation.

Value

A numeric vector.

Examples

x <- sin(seq(0, 1, length.out = 1000) * 2 * pi * 6)
y <- x[1:100]
out1 <- RunningCov(x, y, circular = TRUE)
out2 <- RunningCov(x, y, circular = FALSE)
plot(out1, type = "l"); points(out2, col = "red")


Fast Running L2 Norm Computation

Description

Computes running L2 norm between between time-series x and short-time pattern y.

Usage

RunningL2Norm(x, y, circular = FALSE)

Arguments

x

A numeric vector.

y

A numeric vector, of equal or shorter length than x.

circular

logical; whether running L2 norm is computed assuming circular nature of x time-series (see Details).

Details

Computes running L2 norm between between time-series x and short-time pattern y. The length of output vector equals the length of x. Parameter circular determines whether x time-series is assumed to have a circular nature. Assume l_x is the length of time-series x, l_y is the length of short-time pattern y.

If circular equals TRUE then

If circular equals FALSE then

See runstats.demo(func.name = "RunningL2Norm") for a detailed presentation.

Value

A numeric vector.

Examples

## Ex.1.
x <- sin(seq(0, 1, length.out = 1000) * 2 * pi * 6)
y1 <- x[1:100] + rnorm(100)
y2 <- rnorm(100)
out1 <- RunningL2Norm(x, y1)
out2 <- RunningL2Norm(x, y2)
plot(out1, type = "l"); points(out2, col = "blue")
## Ex.2.
x <- sin(seq(0, 1, length.out = 1000) * 2 * pi * 6)
y <- x[1:100] + rnorm(100)
out1 <- RunningL2Norm(x, y, circular = TRUE)
out2 <- RunningL2Norm(x, y, circular = FALSE)
plot(out1, type = "l"); points(out2, col = "red")

Fast Running Mean Computation

Description

Computes running sample mean of a time-series x in a fixed length window.

Usage

RunningMean(x, W, circular = FALSE)

Arguments

x

A numeric vector.

W

A numeric scalar; length of x window over which sample mean is computed.

circular

Logical; whether running sample mean is computed assuming circular nature of x time-series (see Details).

Details

The length of output vector equals the length of x vector. Parameter circular determines whether x time-series is assumed to have a circular nature. Assume l_x is the length of time-series x, W is a fixed length of x time-series window.

If circular equals TRUE then

If circular equals FALSE then

See runstats.demo(func.name = "RunningMean") for a detailed presentation.

Value

A numeric vector.

Examples

x <- rnorm(10)
RunningMean(x, 3, circular = FALSE)
RunningMean(x, 3, circular = TRUE)


Fast Running Standard Deviation Computation

Description

Computes running sample standard deviation of a time-series x in a fixed length window.

Usage

RunningSd(x, W, circular = FALSE)

Arguments

x

A numeric vector.

W

A numeric scalar; length of x window over which sample variance is computed.

circular

Logical; whether running sample standard deviation is computed assuming circular nature of x time-series (see Details).

Details

The length of output vector equals the length of x vector. Parameter circular determines whether x time-series is assumed to have a circular nature. Assume l_x is the length of time-series x, W is a fixed length of x time-series window.

If circular equals TRUE then

If circular equals FALSE then

See runstats.demo(func.name = "RunningSd") for a detailed presentation.

Value

A numeric vector.

Examples

x <- rnorm(10)
RunningSd(x, 3, circular = FALSE)
RunningSd(x, 3, circular = FALSE)


Fast Running Variance Computation

Description

Computes running sample variance of a time-series x in a fixed length window.

Usage

RunningVar(x, W, circular = FALSE)

Arguments

x

A numeric vector.

W

A numeric scalar; length of x window over which sample variance is computed.

circular

Logical; whether running sample variance is computed assuming circular nature of x time-series (see Details).

Details

The length of output vector equals the length of x vector. Parameter circular determines whether x time-series is assumed to have a circular nature. Assume l_x is the length of time-series x, W is a fixed length of x time-series window.

If circular equals TRUE then

If circular equals FALSE then

See runstats.demo(func.name = "RunningVar") for a detailed presentation.

Value

A numeric vector.

Examples

x <- rnorm(10)
RunningVar(x, W = 3, circular = FALSE)
RunningVar(x, W = 3, circular = TRUE)


Demo visualization of package functions

Description

Generates demo visualization of output of methods for computing running statistics.

Usage

runstats.demo(func.name = "RunningCov")

Arguments

func.name

Character value; one of the following:

  • "RunningMean",

  • "RunningSd",

  • "RunningVar",

  • "RunningCov",

  • "RunningCor",

  • "RunningL2Norm".

Value

NULL

Examples

## Not run: 
runstats.demo(func.name = "RunningMean")
runstats.demo(func.name = "RunningSd")
runstats.demo(func.name = "RunningVar")
runstats.demo(func.name = "RunningCov")
runstats.demo(func.name = "RunningCor")
runstats.demo(func.name = "RunningL2Norm")

## End(Not run)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.