Package 'runstats'

Title: Fast Computation of Running Statistics for Time Series
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).
Authors: Marta Karas [aut, cre] (ORCID: <https://orcid.org/0000-0001-5889-3970>), Jacek Urbanek [aut] (ORCID: <https://orcid.org/0000-0002-1890-8899>), John Muschelli [ctb] (ORCID: <https://orcid.org/0000-0001-6469-1750>), Lacey Etzkorn [ctb]
Maintainer: Marta Karas <[email protected]>
License: GPL-3
Version: 1.1.0
Built: 2026-05-17 05:54:44 UTC
Source: https://github.com/martakarass/runstats

Help Index

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 lxl_x is the length of time-series x, lyl_y is the length of short-time pattern y.

If circular equals TRUE then

  • first element of the output vector corresponds to sample correlation between x[1:l_y] and y,

  • last element of the output vector corresponds to sample correlation between c(x[l_x], x[1:(l_y - 1)]) and y.

If circular equals FALSE then

  • first element of the output vector corresponds to sample correlation between x[1:l_y] and y,

  • the lx−W+1l_x - W + 1-th element of the output vector corresponds to sample correlation between x[(l_x - l_y + 1):l_x],

  • last W-1 elements of the output vector are filled with NA.

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 lxl_x is the length of time-series x, lyl_y is the length of short-time pattern y.

If circular equals TRUE then

  • first element of the output vector corresponds to sample covariance between x[1:l_y] and y,

  • last element of the output vector corresponds to sample covariance between c(x[l_x], x[1:(l_y - 1)]) and y.

If circular equals FALSE then

  • first element of the output vector corresponds to sample covariance between x[1:l_y] and y,

  • the lx−W+1l_x - W + 1-th last element of the output vector corresponds to sample covariance between x[(l_x - l_y + 1):l_x],

  • last W-1 elements of the output vector are filled with NA.

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 lxl_x is the length of time-series x, lyl_y is the length of short-time pattern y.

If circular equals TRUE then

  • first element of the output vector corresponds to sample L2 norm between x[1:l_y] and y,

  • last element of the output vector corresponds to sample L2 norm between c(x[l_x], x[1:(l_y - 1)]) and y.

If circular equals FALSE then

  • first element of the output vector corresponds to sample L2 norm between x[1:l_y] and y,

  • the lx−W+1l_x - W + 1-th element of the output vector corresponds to sample L2 norm between x[(l_x - l_y + 1):l_x],

  • last W-1 elements of the output vector are filled with NA.

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 lxl_x is the length of time-series x, W is a fixed length of x time-series window.

If circular equals TRUE then

  • first element of the output time-series corresponds to sample mean of x[1:W],

  • last element of the output time-series corresponds to sample mean of c(x[l_x], x[1:(W - 1)]).

If circular equals FALSE then

  • first element of the output time-series corresponds to sample mean of x[1:W],

  • lx−W+1l_x - W + 1-th element of the output time-series corresponds to sample mean of x[(l_x - W + 1):l_x],

  • last W-1 elements of the output time-series are filled with NA.

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 lxl_x is the length of time-series x, W is a fixed length of x time-series window.

If circular equals TRUE then

  • first element of the output time-series corresponds to sample standard deviation of x[1:W],

  • last element of the output time-series corresponds to sample standard deviation of c(x[l_x], x[1:(W - 1)]).

If circular equals FALSE then

  • first element of the output time-series corresponds to sample standard deviation of x[1:W],

  • the lx−W+1l_x - W + 1-th element of the output time-series corresponds to sample standard deviation of x[(l_x - W + 1):l_x],

  • last W-1 elements of the output time-series are filled with NA.

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 lxl_x is the length of time-series x, W is a fixed length of x time-series window.

If circular equals TRUE then

  • first element of the output time-series corresponds to sample variance of x[1:W],

  • last element of the output time-series corresponds to sample variance of c(x[l_x], x[1:(W - 1)]).

If circular equals FALSE then

  • first element of the output time-series corresponds to sample variance of x[1:W],

  • the lx−W+1l_x - W + 1-th element of the output time-series corresponds to sample variance of x[(l_x - W + 1):l_x],

  • last W-1 elements of the output time-series are filled with NA.

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)