sim_daily {tssim}

Simulate a daily seasonal 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])