zoo_smooth_exponential: Exponential Smoothing of Zoo Time Series

In BlasBenito/distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Exponential Smoothing of Zoo Time Series

Description

Applies exponential smoothing to a zoo time series object, where each value is a weighted average of the current value and past smoothed values. This method is useful for reducing noise in time series data while preserving the general trend.

Usage

zoo_smooth_exponential(x = NULL, alpha = 0.2)

Arguments

x	(required, zoo object) time series to smooth Default: NULL
alpha	(required, numeric) Smoothing factor in the range (0, 1]. Determines the weight of the current value relative to past values. A higher value gives more weight to recent observations, while a lower value gives more weight to past observations. Default: 0.2

Value

zoo object

See Also

Other zoo_functions: zoo_aggregate(), zoo_name_clean(), zoo_name_get(), zoo_name_set(), zoo_permute(), zoo_plot(), zoo_resample(), zoo_smooth_window(), zoo_time(), zoo_to_tsl(), zoo_vector_to_matrix()

Examples

x <- zoo_simulate()

x_smooth <- zoo_smooth_exponential(
  x = x,
  alpha = 0.2
)

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

BlasBenito/distantia documentation built on Feb. 21, 2025, 2:48 a.m.