Data Transformation: Polynomial Linear Trend of Zoo Time Series

Fits a polynomial linear model on each column of a zoo object using time as a predictor, and predicts the outcome to return the polynomial trend of the time series. This method is a useful alternative to f_trend_linear when the overall. trend of the time series does not follow a straight line.

Usage

f_trend_poly(x = NULL, degree = 2, center = TRUE, ...)

Arguments

x

(required, zoo object) Zoo time series object to transform.

degree

(optional, integer) Degree of the polynomial. Default: 2

center

(required, logical) If TRUE, the output is centered at zero. If FALSE, it is centered at the data mean. Default: TRUE

...

(optional, additional arguments) Ignored in this function.

Value

zoo object

See also

Other tsl_transformation: f_binary(), f_clr(), f_detrend_difference(), f_detrend_linear(), f_detrend_poly(), f_hellinger(), f_list(), f_log(), f_percent(), f_proportion(), f_proportion_sqrt(), f_rescale_global(), f_rescale_local(), f_scale_global(), f_scale_local(), f_trend_linear()

Examples

x <- zoo_simulate(cols = 2)

y <- f_trend_poly(
  x = x
)

if(interactive()){
  zoo_plot(x)
  zoo_plot(y)
}