Linearize a time series using a fractional airline model — fractional_airline

This function estimates a (fractional) airline RegARIMA model and returns the linearized series together with regression effects, outlier components, estimation results and likelihood diagnostics. It is typically used as a preprocessing step prior to AMB or UCM-based decompositions.

Usage

fractional_airline_estimation(
  series,
  period,
  xreg = NULL,
  ndiff = 2,
  ar = FALSE,
  mean = FALSE,
  outliers = NULL,
  critical_value = 6,
  precision = 1e-12,
  deps = 1e-04,
  approximate_hessian = FALSE,
  nfcasts = 0,
  log = FALSE,
  series_time = NULL
)

Arguments

series: input time series.
period: numeric vector of seasonal periods. Each value must be a positive real number (e.g. 7 for weekly, 365.2425 for annual seasonality).
xreg: optional matrix of user-defined regression variables (e.g. calendar regressors built using rjd3toolkit).
ndiff: integer specifying the number of regular differences. Default is 2.
ar: logical. If TRUE, an autoregressive component is included in the model. Default is FALSE.
mean: logical. If TRUE, a mean component is included in the model. Default is FALSE.
outliers: character vector specifying the types of outliers to detect. Possible values include "AO", "LS" and "WO". Default is NULL (no automatic outlier detection).
critical_value: numeric. Critical value for automatic outlier detection. Larger values imply more conservative detection. Default is 6.
precision: numeric. Precision of the likelihood optimization. Default is 1e-12.
deps: step in the computation of the numerical derivatives, used in the optimisation routine. Default:1e-4
approximate_hessian: logical. If TRUE, compute an approximate Hessian matrix based on the optimization procedure. Default is FALSE.
nfcasts: number of forecasts. Default is 0.
log: logical. If TRUE, the model is estimated on the log-scale. Default is FALSE.
series_time: optional vector of time indices associated with series.

Value

An object of class "hf_estimation" containing:

the original and linearized series,
estimated regression effects and outlier components,
model parameters and covariance matrices,
likelihood and diagnostic information.

Details

Automatic outlier detection can be enabled by specifying the outlier types and a critical value for the detection threshold.

Examples

# Simulated examples with and without regressor
set.seed(125)
reg <- data.frame(
   reg1 = round(runif(1000, min = 0, max = 1))*2-1,
 reg2 = round(runif(1000, min = 0, max = 1))*2-1
)

y = 100 + 2*reg$reg1 -2*reg$reg2 + rnorm(nrow(reg))

# input data
data <- list(
   series = y,
   date = seq.Date(from = as.Date("2020-01-01"),
   by = "day",
                   length.out = 1000)
)

# Linearize the series using weekly and annual periodicities
est <- fractional_airline_estimation(
   data$series,
   period = c(7, 30.4),
   log = FALSE,
   xreg = reg,
   series_time = data$date
)
#> Error in .jcall(obj = "jdplus/toolkit/base/api/math/matrices/Matrix",     returnSig = "Ljdplus/toolkit/base/api/math/matrices/Matrix;",     method = "of", .jarray(as.double(s)), as.integer(sdim[1]),     as.integer(sdim[2])): RcallMethod: cannot determine object class

est
#> Error: object 'est' not found

est2 <- fractional_airline_estimation(
  data$series,
  period = c(7, 30.4),
  log = FALSE,
  series_time = data$date
)
#> Error in .jcheck(): java.lang.UnsupportedClassVersionError: jdplus/toolkit/base/api/math/matrices/Matrix 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

est2
#> Error: object 'est2' not found