Perform an Arima Model Based (AMB) decomposition

Performs an Arima Model Based (AMB) decomposition using a (fractional) airline model, suitable for high-frequency time series. The method decomposes the input series into trend, seasonal and irregular components, with optional signal–noise decomposition.

Usage

fractional_airline_decomposition(
  series,
  period,
  sn = FALSE,
  stde = FALSE,
  nbcasts = 0,
  nfcasts = 0,
  eps = 1e-09,
  deps = 1e-04,
  log = FALSE,
  series_time = NULL
)

Arguments

series

input time series.

period

period of the seasonal component, any positive real number.

sn

decomposition into signal and noise (2 components only). The signal is the seasonally adjusted series and the noise the seasonal component. Default: FALSE.

stde

Boolean: TRUE: compute standard deviations of the components. In some cases (e.g. memory limits), it is currently not possible to compute them. Default: FALSE.

nbcasts

number of backcasts. Default: 0.

nfcasts

number of forecasts. Default: 0.

eps

precision of the optimisation routine. Default:1e-9.

deps

step in the computation of the numerical derivatives, used in the optimisation routine. Default:1e-4

log

logical indicating whether the series is on the log scale. Default: FALSE.

series_time

vector of times at which `series` is indexed. Optional.

Value

An object containing the AMB decomposition results, including the estimated components and, if requested, their standard deviations.

Details

If `sn = TRUE`, the decomposition is restricted to two components only (signal and noise). When `stde = TRUE`, the computation of standard deviations may fail for long series or high-frequency data due to memory constraints.

Examples

series <- rnorm(70)+100

### Example with a daily time series with a day-of-the-week effect
weekday <- rjd3highfreq::fractional_airline_decomposition(
  series,
  period = 7,
  log = TRUE,
  series_time = seq.Date(from=as.Date("2025-01-01"),
                         by = "days",
                         length.out = length(series))
)
#> Error in rJava::.jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",     "Ljdplus/highfreq/base/core/extendedairline/decomposition/LightExtendedAirlineDecomposition;",     "decompose", as.numeric(series), as.numeric(period), sn,     stde, as.integer(nbcasts), as.integer(nfcasts), as.numeric(eps),     as.numeric(deps)): RcallMethod: cannot determine object class

### Example with a weekly time series
series <-  rnorm(200)+100
weekly <- rjd3highfreq::fractional_airline_decomposition(
  series,
  period = 52.18,
  log =  TRUE,
  series_time = seq.Date(from=as.Date("2025-01-01"),
                         by = "days",
                         length.out = length(series))
)
#> Error in .jcheck(): java.lang.UnsupportedClassVersionError: jdplus/highfreq/base/r/FractionalAirlineProcessor has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 61.0