sim_daily: Simulate a daily seasonal series

Description

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

Usage

sim_daily(
  N,
  sd = 5,
  moving = TRUE,
  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_tau7 = 0.01,
  beta_tau31 = 0,
  beta_tau365 = 0.2,
  start = c(2020, 1),
  multiplicative = TRUE,
  extra_smooth = FALSE,
  calendar = list(which = "Easter", from = -2, to = 2),
  outlier = NULL,
  timewarping = FALSE,
  as_index = FALSE
)

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
sfac365: The day-of-the-year effect
cfac: The calendar effects
outlier: The outlier effects

Arguments

N: length in years
sd: Standard deviation for all seasonal factors
moving: Is the seasonal pattern allowed to change over time
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 shock to seasonal factor
month_change_sd: Standard deviation of shock to seasonal factor
year_change_sd: Standard deviation of shock to seasonal factor
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_tau7: Persistance wrt to one year/cycle before of the seasonal change for day-of-the-week
beta_tau31: Persistance wrt to one year/cycle before of the seasonal change for day-of-the-month
beta_tau365: Persistance wrt to one year/cycle before of the seasonal change for day-of-the-year
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)

Author

Daniel Ollech

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. The parameters that can be set for calendar and outlier are those defined in sim_outlier and sim_calendar.

References

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

Examples

Run this code

x=sim_daily(5, sd=10, multiplicative=TRUE, outlier=list(k=5, type=c("AO", "LS")))
ts.plot(x[,1])

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