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Calendarization

Source: R/calendarization.R
calendarization.Rd

Time series data do not always coincide with calendar periods (e.g., fiscal years starting in March-April or retail data collected in non-monthly intervals). Calendarization is the process of transforming the values of a flow time series observed over varying time intervals into values that cover given calendar intervals such as month, quarter or year. The process involves two steps. At first, a state-space representation of the Denton proportional first difference (PFD) method is considered to perform a temporal disaggregation of the observed data into daily values. After that, the resulting daily values are aggregated into the desired calendar reference periods.

Usage

calendarization(
  calendarobs,
  freq,
  start = NULL,
  end = NULL,
  dailyweights = NULL,
  stde = FALSE
)

Arguments

calendarobs

A named list containing the observed data. The list must consist of three elements: start, end and value, where the first two indicate the starting and ending dates of the observation. See the example.

freq

An integer specifying the annual frequency. If set to 0, only the daily series is computed.

start

The starting day of the calendarization. This date may precede the first observed data (retropolation).

end

The ending day of the calendarization. This date may exceed the last observed data (extrapolation).

dailyweights

A numeric vector of daily indicator values (or weights). The vector must have the same length as the requested daily series. When available, these weights typically reflects daily levels of activity, which may vary due to seasonality, trading day effects, or other calendar effects such as public holidays.

stde

Boolean. If TRUE, the function also returns the standard errors associated with the results. The default is FALSE.

Value

A list containing the disaggregated daily values, the final aggregated series, and their associated standard errors if requested.

References

Quenneville, B., Picard F., Fortier S. (2012). Calendarization with interpolating splines and state space models. Statistics Canada, Appl. Statistics (2013) 62, part 3, pp 371-399.

See also

For more information, see the vignette:

utils::browseVignettes(), e.g. browseVignettes(package = "rjd3bench")

Examples


# Example 1 (from Quenneville et al (2012))

## Observed data
obs_1 <- list(
    list(start = "2009-02-18", end = "2009-03-17", value = 9000),
    list(start = "2009-03-18", end = "2009-04-14", value = 5000),
    list(start = "2009-04-15", end = "2009-05-12", value = 9500),
    list(start = "2009-05-13", end = "2009-06-09", value = 7000))

## a) calendarization in absence of daily indicator values (or weights)
cal_1a <- calendarization(obs_1, 12, end = "2009-06-30", dailyweights = NULL, stde = TRUE)

ym_1a <- cal_1a$rslt
eym_1a <- cal_1a$erslt
yd_1a <- cal_1a$days
eyd_1a <- cal_1a$edays

## b) calendarization in presence of daily indicator values (or weights)
x <- rep(c(1.0, 1.2, 1.8 , 1.6, 0.0, 0.6, 0.8), 19)
cal_1b <- calendarization(obs_1, 12, end = "2009-06-30", dailyweights = x, stde = TRUE)

ym_1b <- cal_1b$rslt
eym_1b <- cal_1b$erslt
yd_1b <- cal_1b$days
eyd_1b <- cal_1b$edays

# Example 2 (incl. negative value)

obs_2 <- list(
    list(start = "1980-01-01", end = "1989-12-31", value = 100),
    list(start = "1990-01-01", end = "1999-12-31", value = -10),
    list(start = "2000-01-01", end = "2002-12-31", value = 50))

cal_2 <- calendarization(obs_2, 4, end = "2003-12-31")

yq_2 <- cal_2$rslt