Rolling functions

Description

Fast rolling functions to calculate aggregates on a sliding window. For a user-defined rolling function see frollapply. For "time-aware" (irregularly spaced time series) rolling function see frolladapt.

Usage

  frollmean(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"),
    na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)
  frollsum(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"),
    na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)
  frollmax(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"),
    na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)
  frollmin(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"),
    na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)
  frollprod(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"),
    na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)
  frollmedian(x, n, fill=NA, algo=c("fast","exact"), align=c("right","left","center"),
    na.rm=FALSE, has.nf=NA, adaptive=FALSE, partial=FALSE, give.names=FALSE, hasNA)

Arguments

Details

froll* functions accept vector, list, data.frame or data.table. Functions operate on a single vector; when passing a non-atomic input, then function is applied column-by-column, not to the complete set of columns at once.

Argument n allows multiple values to apply rolling function on multiple window sizes. If adaptive=TRUE, then n can be a list to specify multiple window sizes for adaptive rolling computation. See Adaptive rolling functions section below for details.

When multiple columns and/or multiple window widths are provided, then computations run in parallel. The exception is for algo="exact", which runs in parallel even for single column and single window width. By default, data.table uses only half of available CPUs, see setDTthreads for details on how to tune CPU usage.

Adaptive rolling functions are a special case where each observation has its own corresponding rolling window width. Due to the logic of adaptive rolling functions, the following restrictions apply:

• align only "right".

• if list of vectors is passed to x, then all vectors within it must have equal length.

When multiple columns or multiple windows width are provided, then they are run in parallel. The exception is for algo="exact", which runs in parallel already.

Setting options(datatable.verbose=TRUE) will display various information about how rolling function processed. It will not print information in real-time but only at the end of the processing.

Value

For a non vectorized input (x is not a list, and n specify single rolling window) a vector is returned, for convenience. Thus, rolling functions can be used conveniently within data.table syntax. For a vectorized input a list is returned.

has.nf argument

has.nf can be used to speed up processing in cases when it is known if x contains (or not) non-finite values (NA, NaN, Inf, -Inf).

• Default has.nf=NA uses faster implementation that does not support non-finite values, but when non-finite values are detected it will re-run non-finite supported implementation.

• has.nf=TRUE uses non-finite aware implementation straightaway.

• has.nf=FALSE uses faster implementation that does not support non-finite values. Then depending on the rolling function it will either:

  • (mean, sum, prod) detect non-finite, re-run non-finite aware.

  • (max, min, median) does not detect non-finites and may silently produce an incorrect answer.

  In general has.nf=FALSE && any(!is.finite(x)) should be considered as undefined behavior. Therefore has.nf=FALSE should be used with care.

Implementation

Each rolling function has 4 different implementations. First factor that decides which implementation is used is the adaptive argument (either TRUE or FALSE), see section below for details. Then for each of those two algorithms there are usually two implementations depending on the algo argument.

• algo="fast" uses "online", single pass, algorithm.

  • max and min rolling function will not do only a single pass but, on average, they will compute length(x)/n nested loops. The larger the window, the greater the advantage over the exact algorithm, which computes length(x) nested loops. Note that exact uses multiple CPUs so for a small window sizes and many CPUs it may actually be faster than fast. However, in such cases the elapsed timings will likely be far below a single second.

  • median will use a novel algorithm described by Jukka Suomela in his paper Median Filtering is Equivalent to Sorting (2014). See references section for the link. Implementation here is extended to support arbitrary length of input and an even window size. Despite extensive validation of results this function should be considered experimental. When missing values are detected it will fall back to slower algo="exact" implementation.

  • Not all functions have fast implementation available. As of now adaptive max, adaptive min and adaptive median do not have fast implementation, therefore it will automatically fall back to exact implementation. datatable.verbose option can be used to check that.

• algo="exact" will make the rolling functions use a more computationally-intensive algorithm. For each observation in the input vector it will compute a function on a rolling window from scratch (complexity O(n^2)).

  • Depeneding on the function, this algorithm may suffers less from floating point rounding error (the same consideration applies to base mean).

  • In case of mean (and possibly other functions in future), it will additionally make extra pass to perform floating point error correction. Error corrections might not be truly exact on some platforms (like Windows) when using multiple threads.

Adaptive rolling functions

Adaptive rolling functions are a special case where each observation has its own corresponding rolling window width. Therefore, values passed to n argument must be series corresponding to observations in x. If multiple windows are meant to be computed, then a list of integer vectors is expected; each list element must be an integer vector of window size corresponding to observations in x; see Examples. Due to the logic or implementation of adaptive rolling functions, the following restrictions apply

• align does not support "center".

• if list of vectors is passed to x, then all vectors within it must have equal length due to the fact that length of adaptive window widths must match the length of vectors in x.

partial argument

partial=TRUE is used to calculate rolling moments only within the input itself. That is, at the boundaries (say, observation 2 for n=4 and align="right"), we don't consider observations before the first as "missing", but instead shrink the window to be size n=2. In practice, this is the same as an adaptive window, and could be accomplished, albeit less concisely, with a well-chosen n and adaptive=TRUE. In fact, we implement partial=TRUE using the same algorithms as adaptive=TRUE. Therefore partial=TRUE inherits the limitations of adaptive rolling functions, see above. Adaptive functions use more complex algorithms; if performance is important, partial=TRUE should be avoided in favour of computing only missing observations separately after the rolling function; see examples.

zoo package users notice

Users coming from most popular package for rolling functions zoo might expect following differences in data.table implementation

• rolling function will always return result of the same length as input.

• fill defaults to NA.

• fill accepts only constant values. It does not support for na.locf or other functions.

• align defaults to "right".

• na.rm is respected, and other functions are not needed when input contains NA.

• integers and logical are always coerced to numeric.

• when adaptive=FALSE (default), then n must be a numeric vector. List is not accepted.

• when adaptive=TRUE, then n must be vector of length equal to nrow(x), or list of such vectors.

Note

Be aware that rolling functions operate on the physical order of input. If the intent is to roll values in a vector by a logical window, for example an hour, or a day, then one has to ensure that there are no gaps in the input, or use adaptive rolling function to handle gaps, for which we provide helper function frolladapt to generate adaptive window size.

References

Round-off error, "Median Filtering is Equivalent to Sorting" by Jukka Suomela

See Also

frollapply, frolladapt, shift, data.table, setDTthreads

Examples

# single vector and single window
frollmean(1:6, 3)

d = as.data.table(list(1:6/2, 3:8/4))
# rollmean of single vector and single window
frollmean(d[, V1], 3)
# multiple columns at once
frollmean(d, 3)
# multiple windows at once
frollmean(d[, .(V1)], c(3, 4))
# multiple columns and multiple windows at once
frollmean(d, c(3, 4))
## three calls above will use multiple cores when available

# other functions
frollsum(d, 3:4)
frollmax(d, 3:4)
frollmin(d, 3:4)
frollprod(d, 3:4)
frollmedian(d, 3:4)

# partial=TRUE
x = 1:6/2
n = 3
ans1 = frollmean(x, n, partial=TRUE)
# same using adaptive=TRUE
an = function(n, len) c(seq.int(n), rep.int(n, len-n))
ans2 = frollmean(x, an(n, length(x)), adaptive=TRUE)
all.equal(ans1, ans2)
# speed up by using partial only for incomplete observations
ans3 = frollmean(x, n)
ans3[seq.int(n-1L)] = frollmean(x[seq.int(n-1L)], n, partial=TRUE)
all.equal(ans1, ans3)

# give.names
frollsum(list(x=1:5, y=5:1), c(tiny=2, big=4), give.names=TRUE)

# has.nf=FALSE should be used with care
frollmax(c(1,2,NA,4,5), 2)
frollmax(c(1,2,NA,4,5), 2, has.nf=FALSE)

# performance vs exactness
set.seed(108)
x = sample(c(rnorm(1e3, 1e6, 5e5), 5e9, 5e-9))
n = 15
ma = function(x, n, na.rm=FALSE) {
  ans = rep(NA_real_, nx<-length(x))
  for (i in n:nx) ans[i] = mean(x[(i-n+1):i], na.rm=na.rm)
  ans
}
fastma = function(x, n, na.rm) {
  if (!missing(na.rm)) stop("NAs are unsupported, wrongly propagated by cumsum")
  cs = cumsum(x)
  scs = shift(cs, n)
  scs[n] = 0
  as.double((cs-scs)/n)
}
system.time(ans1<-ma(x, n))
system.time(ans2<-fastma(x, n))
system.time(ans3<-frollmean(x, n))
system.time(ans4<-frollmean(x, n, algo="exact"))
system.time(ans5<-frollapply(x, n, mean))
anserr = list(
  fastma = ans2-ans1,
  froll_fast = ans3-ans1,
  froll_exact = ans4-ans1,
  frollapply = ans5-ans1
)
errs = sapply(lapply(anserr, abs), sum, na.rm=TRUE)
sapply(errs, format, scientific=FALSE) # roundoff