Seasonally adjust daily time series using the dsa approach
dsa(
series,
span.start = NULL,
model = NULL,
Log = FALSE,
automodel = "reduced",
ic = "bic",
include.constant = FALSE,
fourier_number = 24,
max_fourier = 30,
s.window1 = 53,
s.window2 = 53,
s.window3 = 13,
t.window1 = NULL,
t.window2 = NULL,
t.window3 = NULL,
cval = 7,
robust1 = TRUE,
robust2 = TRUE,
robust3 = TRUE,
regressor = NULL,
forecast_regressor = NULL,
reg_create = NULL,
reg_dummy = NULL,
outlier = TRUE,
outlier_types = c("AO", "LS", "TC"),
delta = 0.7,
model_span = NULL,
feb29 = "sfac",
trend_month = 3,
outer3 = NULL,
inner3 = NULL,
h = 365,
reiterate3 = NULL,
scaler = 1e+07,
mean_correction = TRUE,
progress_bar = TRUE
)Input time series in xts format
Define when seasonal adjustment should begin
ARIMA order of non-seasonal part
Boolean. Should multiplicate or additive model be used
Set of models to be considered for automatic model detection. Either "full" or "reduced" set of fourier regressors included
Information criterion that is used for automodelling. One of "bic", "aic" or "aicc"
Should drift be allowed for model that includes differencing
Number of trigometric regressors to model annual and monthly seasonality
Maximum number of trigonometric regressors allowed if the number is selected automatically, i.e. fourier_number=NULL
STL parameter s.window for the day of the week effect
STL parameter s.window for the day of the month effect
STL parameter s.window for the day of the year effect
STL parameter t.window for the day of the week effect
STL parameter t.window for the day of the month effect
STL parameter t.window for the day of the year effect
Critical value for outlier adjustment
Boolean. Should robust STL be used for the day of the week effect
Boolean. Should robust STL be used for the day of the month effect
Boolean. Should robust STL be used for the day of the year effect
Pre-specified regressors
Pre-specified regressors to be used for forecasting
Names of Holidays for which regressors will be created
If specified dummy variables of specified length are created and used as regressors
Should an outlier adjustment be conducted?
The following are possible: "LS", "TC", "AO", "IO"
The decay rate for TC outliers
Last x years used for regARIMA modelling
How should February 29th be derived: interpolation of adjusted series ("sa") or combined factor ("sfac")
Length of support period for trend estimation
Number of iterations of outer loop in STL for day of the year effect
Number of iterations of inner loop in STL for day of the year effect
Forecast horizon in number of days
Number of total iterations of STL for the day of the year effect
for additive model, if max(abs(series)) > 1e5, scale series
Boolean. Should seasonal factors be standardised so that their mean (over all full cycles) is 0 for additive and 1 for multiplicative models
Boolean. Should a progress bar be displayed
dsa returns a daily object which contains the output of the seasonal adjustment of a daily time series.
output Contains the calendar and seasonally adjusted series, original series, implicit calendar and seasonal component, and Loess based trend as an xts object
fourier_terms The number of sine and cosine terms used to model the seasonal pattern in the RegARIMA model
reg RegARIMA results
info Basic information on transformation (Log/Level), differencing and forecast horizon
stl A list of length 3, containing the STL results of the day-of-week, day-of-the-month and day-of-the-year adjustment, respectively
outlier Result of the outlier adjustment
sa_result The original series and the intermediate adjustment results after the day-of-week adjustment (s1_adjusted), calendar adjustment (s1k1_adjusted), day-of-the-month adjustment (s1k1s2_adjusted), and the final adjusted series after the day-of-the-year adjustment (seas_adj) as an xts object
sa_result2 The original series only adjusted for single components as an xts object. Namely the original series itself (original), the original only adjusted for the day-of-the week (s1_adjusted), calendar (k1_adjusted), day-of-the-month (s2_adjusted), and day-of-the-year (s3_adjusted)
sfac_result The seasonal and calendar components as an xts object. Namely, the day-of-the-week (s1_fac), calendar (cal_fac), day-of-the-month (s2_fac), and day-of-the-year component (s3_fac)
This function can be used to seasonally and calendar adjust daily time series and decomposing the series into a seasonally adjusted series, a day-of-the-week, a moving holiday, a day-of-the-month and a day-of-the-year component.
If mean_correction=TRUE (default), the seasonal and calendar factors are corrected, so that over all full years, the mean of the components is 0 in additive models. They will be close to 1 if a multiplicative decomposition (i.e. Log=TRUE) is used. Deviations from 1 may result, because the mean correction is applied to the components before inverting taking logs.
For long series, the ARIMA modelling and the outlier adjustment may take a long time. It may therefore be a good idea, to specify the ARIMA model used, e.g. model=c(3,1,0). If the series does not contain influential outliers, the outlier adjustment could be skipped by setting outlier=FALSE.
See vignette for further examples.
Ollech, Daniel (2018). Seasonal adjustment of daily time series. Bundesbank Discussion Paper 41/2018.
Ollech, Daniel (2021). Seasonal Adjustment of Daily Time Series. Journal of Time Series Econometrics (forthcoming).
# NOT RUN {
x = daily_sim(n=4)$original # series with length 4 years
res <- dsa(x, cval=7, model=c(3,1,0),fourier_number = 13)
# }
Run the code above in your browser using DataLab