Examples of using runstats package
runstats
Package runstats provides methods for fast computation
of running sample statistics for time series. The methods utilize
Convolution Theorem to compute convolutions via Fast Fourier Transform
(FFT). Implemented running statistics include:
- mean,
- standard deviation,
- variance,
- covariance,
- correlation,
- euclidean distance.
Usage
library(runstats)
## Example: running correlation
x0 <- sin(seq(0, 2 * pi * 5, length.out = 1000))
x <- x0 + rnorm(1000, sd = 0.1)
pattern <- x0[1:100]
out1 <- RunningCor(x, pattern)
out2 <- RunningCor(x, pattern, circular = TRUE)
## Example: running mean
x <- cumsum(rnorm(1000))
out1 <- RunningMean(x, W = 100)
out2 <- RunningMean(x, W = 100, circular = TRUE)Running statistics
To better explain the details of running statistics, package’s
function runstats.demo(func.name) allows to visualize how
the output of each running statistics method is generated. To run the
demo, use func.name being one of the methods’ names:
"RunningMean","RunningSd","RunningVar","RunningCov","RunningCor","RunningL2Norm".
Performance
We use rbenchmark to measure elapsed time of
RunningCov execution, for different lengths of time-series
x and fixed length of the shorter pattern
y.
library(rbenchmark)
set.seed (20181010)
x.N.seq <- 10^(3:7)
x.list <- lapply(x.N.seq, function(N) runif(N))
y <- runif(100)
## Benchmark execution time of RunningCov
out.df <- data.frame()
for (x.tmp in x.list){
out.df.tmp <- benchmark("runstats" = runstats::RunningCov(x.tmp, y),
replications = 10,
columns = c("test", "replications", "elapsed",
"relative", "user.self", "sys.self"))
out.df.tmp$x_length <- length(x.tmp)
out.df.tmp$pattern_length <- length(y)
out.df <- rbind(out.df, out.df.tmp)
}| test | replications | elapsed | relative | user.self | sys.self | x_length | pattern_length |
|---|---|---|---|---|---|---|---|
| runstats | 10 | 0.005 | 1 | 0.004 | 0.001 | 1000 | 100 |
| runstats | 10 | 0.023 | 1 | 0.018 | 0.004 | 10000 | 100 |
| runstats | 10 | 0.194 | 1 | 0.158 | 0.037 | 100000 | 100 |
| runstats | 10 | 1.791 | 1 | 1.656 | 0.125 | 1000000 | 100 |
| runstats | 10 | 20.234 | 1 | 17.660 | 2.514 | 10000000 | 100 |
Compare with a conventional method
To compare RunStats performance with “conventional”
loop-based way of computing running covariance in R, we use
rbenchmark package to measure elapsed time of
RunStats::RunningCov and running covariance implemented
with sapply loop, for different lengths of time-series
x and fixed length of the shorter time-series
y.
## Conventional approach
RunningCov.sapply <- function(x, y){
l_x <- length(x)
l_y <- length(y)
sapply(1:(l_x - l_y + 1), function(i){
cov(x[i:(i+l_y-1)], y)
})
}
set.seed (20181010)
out.df2 <- data.frame()
for (x.tmp in x.list[c(1,2,3,4)]){
out.df.tmp <- benchmark("conventional" = RunningCov.sapply(x.tmp, y),
"runstats" = runstats::RunningCov(x.tmp, y),
replications = 10,
columns = c("test", "replications", "elapsed",
"relative", "user.self", "sys.self"))
out.df.tmp$x_length <- length(x.tmp)
out.df2 <- rbind(out.df2, out.df.tmp)
}Benchmark results
library(ggplot2)
plt1 <-
ggplot(out.df2, aes(x = x_length, y = elapsed, color = test)) +
geom_line() + geom_point(size = 3) + scale_x_log10() +
theme_minimal(base_size = 14) +
labs(x = "Vector length of x",
y = "Elapsed [s]", color = "Method",
title = "Running covariance rbenchmark") +
theme(legend.position = "bottom")
plt2 <-
plt1 +
scale_y_log10() +
labs(y = "Log of elapsed [s]")
cowplot::plot_grid(plt1, plt2, nrow = 1, labels = c('A', 'B'))
Platform information
sessioninfo::platform_info()
#> setting value
#> version R version 4.4.2 (2024-10-31)
#> os Ubuntu 24.04.2 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C
#> ctype en_US.UTF-8
#> tz Etc/UTC
#> date 2025-03-02
#> pandoc 3.2.1 @ /usr/local/bin/ (via rmarkdown)
#> quarto 1.6.40 @ /usr/local/bin/quarto