sliding_ase_univariate: Function to calculate the sliding window ASE for a model...

In josephsdavid/tswgewrapped: Helpful wrappers for 'tswge', 'vars' and 'nnfor' time series packages

Description

Function to calculate the sliding window ASE for a model Supports ARMA, ARIMA, ARUMA (seasonal ARIMA) and Signal Plus Noise Models

Usage

sliding_ase_univariate(
  x,
  phi = 0,
  theta = 0,
  d = 0,
  s = 0,
  linear = NA,
  freq = NA,
  n.ahead = NA,
  batch_size = NA,
  step_n.ahead = TRUE,
  verbose = 0,
  ...
)

Arguments

x	time series realization
phi	phi values associated with the ARIMA model
theta	theta values associated with the ARIMA model
d	differencing 'd' associated with the ARIMA model
s	seasonality 's' of the ARIMA model
linear	(TRUE|FALSE) If using a Signal Plus Noise model, should a linear signal be used?
freq	If using a sinusoidal signal, what is the frequency of the signal?
n.ahead	last n.ahead data points in each batch will be used for prediction and ASE calculations
batch_size	Window Size used
step_n.ahead	Whether to step each batch by n.ahead values (Default = FALSE)
verbose	How much to print during the model building and other processes (Default = 0)
...	any additional arguments to be passed to the forecast functions (e.g. max.p for sigplusnoise model, lambda for ARUMA models)

Value

Named list 'ASEs' - ASE values 'time_test_start' - Time Index indicating start of test time corresponding to the ASE values 'time_test_end' - Time Index indicating end of test time corresponding to the ASE values 'batch_num' - Indicates the batch number for each ASE value 'f' - Forecasts for each batch 'll' - Lower Forecast Limit for each batch 'ul' - Upper Forecast Limit for each batch 'time.forecasts' - Time Corresponding to each forecast, upper and lower limit values

josephsdavid/tswgewrapped documentation built on July 31, 2020, 9:36 a.m.