ts_fil_lowess: Lowess Smoothing

In tspredit: Time Series Prediction with Integrated Tuning

Lowess Smoothing

Description

It is a smoothing method that preserves the primary trend of the original observations and is used to remove noise and spikes in a way that allows data reconstruction and smoothing.

Usage

ts_fil_lowess(f = 0.2)

Arguments

f	smoothing parameter. The larger this value, the smoother the series will be. This provides the proportion of points on the plot that influence the smoothing.

Value

a ts_fil_lowess object.

Examples

# time series with noise
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2*tsd$y[9]

# filter
filter <- ts_fil_lowess(f = 0.2)
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# plot
plot_ts_pred(y=tsd$y, yadj=y)

tspredit documentation built on June 22, 2025, 5:07 p.m.