Reduce the number of observations in a month using time warping / stretching. Only relevant if a daily time series is simulated
.stretch_re(seas_component)Returns a xts time series containing the day-of-the-month effect.
Seasonal component for day-of-the-month
Daniel Ollech
Usually time warping would be used to stretch the number of observations of a time series in a given interval to more observations. Here it is used to reduce the number of observations (31) to the number of days in a given month while maintaining the underlying trajectory of the data. This is done by first creating a very long time series for each month, interpolating missing values by spline interpolation and then reducing the number of observations to the number suitable for a given month.
Ollech, D. (2021). Seasonal adjustment of daily time series. Journal of Time Series Econometrics. tools:::Rd_expr_doi("10.1515/jtse-2020-0028")