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normalise_xts

Source: R/normalise_xts.R
normalise_xts.Rd

Empirical normal-score transform of an xts time series: values are mapped to standard normal quantiles via their empirical cumulative distribution function (ranking with average ties). Optionally applied per calendar month to preserve seasonal structure in cyclo-stationary processes.

Usage

normalise_xts(
  ts,
  dist_period = "monthly",
  ignore_zeros = FALSE,
  zero_threshold = 0.01
)

Arguments

ts

An xts object containing the time series data. Multi-column xts are supported; each column is normalised independently.

dist_period

Character; the distribution period for normalisation. Use "monthly" for per-calendar-month normalisation or NA for the entire series. Default "monthly".

ignore_zeros

Logical; if TRUE, zero values (at or below zero_threshold) are excluded before normalisation. Default FALSE.

zero_threshold

Numeric; threshold below which values are treated as zero. Default 0.01.

Value

A named list of normalised xts objects, one per column of the input.

Examples

# Synthetic daily precipitation
set.seed(42)
n <- 365 * 3
dates <- seq(as.POSIXct("2000-01-01", tz = "UTC"), by = "day", length.out = n)
precip <- pmax(0, rnorm(n, mean = 3, sd = 5))
ts <- xts::xts(precip, order.by = dates)

# Monthly normal-score transform
ns <- normalise_xts(ts, dist_period = "monthly")
hist(zoo::coredata(ns[[1]]), main = "Normalised values")


# Full-series transform
ns_full <- normalise_xts(ts, dist_period = NA)