sim_daily: Simulate a daily seasonal series

In tssim: Simulation of Daily and Monthly Time Series

Description

Simulate a daily seasonal series as described in Ollech (2021).

Usage

sim_daily(
  N,
  sd = 2.5,
  change_sd = 0.05,
  week_sd = NA,
  month_sd = NA,
  year_sd = NA,
  week_change_sd = NA,
  month_change_sd = NA,
  year_change_sd = NA,
  innovations_sd = 1,
  sa_sd = NA,
  model = list(order = c(3, 1, 1), ma = 0.5, ar = c(0.2, -0.4, 0.1)),
  beta_1 = 0.9,
  beta_tau = 0,
  start = c(2020, 1),
  multiplicative = TRUE,
  extra_smooth = FALSE,
  calendar = list(which = "Easter", from = -2, to = 2),
  outlier = NULL,
  timewarping = TRUE,
  as_index = FALSE
)

Arguments

N	length in years
sd	Standard deviation for all seasonal factors
change_sd	Standard deviation of simulated change for all seasonal factors
week_sd	Standard deviation of the seasonal factor for day-of-the-week
month_sd	Standard deviation of the seasonal factor for day-of-the-month
year_sd	Standard deviation of the seasonal factor for day-of-the-year
week_change_sd	Standard deviation of simulated change to seasonal factor for day-of-the-week
month_change_sd	Standard deviation of simulated change to seasonal factor for month-of-the-week
year_change_sd	Standard deviation of simulated change to seasonal factor for year-of-the-week
innovations_sd	Standard deviation of the innovations used in the non-seasonal regarima model
sa_sd	Standard deviation of the non-seasonal time series
model	Model for non-seasonal time series. A list.
beta_1	Persistance wrt to previous period of the seasonal change
beta_tau	Persistance wrt to one year/cycle before of the seasonal change
start	Start date of output time series
multiplicative	Boolean. Should multiplicative seasonal factors be simulated
extra_smooth	Boolean. Should the seasonal factors be smooth on a period-by-period basis
calendar	Parameters for calendar effect, a list, see sim_calendar
outlier	Parameters for outlier effect, a list, see sim_outlier
timewarping	Should timewarping be used to obtain the day-of-the-month factors
as_index	Shall series be made to look like an index (i.e. shall values be relative to reference year = second year)

Details

Standard deviation of the seasonal factor is in percent if a multiplicative time series model is assumed. Otherwise it is in unitless. Using a non-seasonal ARIMA model for the initialization of the seasonal factor does not impact the seasonality of the time series. It can just make it easier for human eyes to grasp the seasonal nature of the series. The definition of the ar and ma parameter needs to be inline with the chosen model. If only change_sd is specified, the change parameters for the single seasonal factors are set individually as change_sd/365*(length of seasonal cycle) The parameters that can be set for calendar and outlier are those defined in sim_outlier and sim_calendar.

Value

Multiple simulated daily time series of class xts including:

original

The original series

seas_adj

The original series without calendar and seasonal effects

sfac7

The day-of-the-week effect

sfac31

The day-of-the-month effect

cfac

The calendar effects

outlier

The outlier effects

Author(s)

Daniel Ollech

References

Ollech, D. (2021). Seasonal adjustment of daily time series. Journal of Time Series Econometrics. doi: 10.1515/jtse-2020-0028

Examples

x=sim_daily(5, multiplicative=TRUE, outlier=list(k=5, type=c("AO", "LS"), effect_size=50))
ts.plot(x[,1])

tssim documentation built on June 25, 2021, 5:06 p.m.