Learn R Programming

timeSeries (version 3022.101.2)

smooth: Smoothes Time Series Objects

Description

Smoothes a 'timeSeries' object.

Usage

smoothLowess(x, f = 0.5, ...) smoothSpline(x, spar = NULL, ...) smoothSupsmu(x, bass = 5, ...)

Arguments

x
an univariate 'timeSeries' object.
f
the lowess smoother span. This gives the proportion of points in the plot which influence the smooth at each value. Larger values give more smoothness.
spar
smoothing parameter, typically (but not necessarily) in (0,1]. By default NULL, i.e. the value will be automatically selected.
bass
controls the smoothness of the fitted curve. Values of up to 10 indicate increasing smoothness.
...
optional arguments to be passed to the underlying smoothers.

Value

returns a bivariate 'timeSeries' object, the first column holds the original time series data, the second the smoothed series.

Details

The functions smoothLowess, smoothSpline, smoothSupsmu allow to smooth timeSerie object. The are interfaces to the function lowess, supmsu. and smooth.spline in R's stats package. The ... arguments allow to pass optional arguments to the underlying stats functions and tailor the smoothing process. We refer to the manual pages of these functions for a proper setting of these options.

Examples

Run this code
## Use Close from MSFT's Price Series  - 
   head(MSFT)
   MSFT.CLOSE <- MSFT[, "Close"]
   head(MSFT.CLOSE)
   
## Plot Original and Smoothed Series by Lowess - 
   MSFT.LOWESS <- smoothLowess(MSFT.CLOSE, f = 0.1)
   head(MSFT.LOWESS)
   plot(MSFT.LOWESS)
   title(main = "Close - Lowess Smoothed")
   
## Plot Original and Smoothed Series by Splines - 
   MSFT.SPLINE <- smoothSpline(MSFT.CLOSE, spar = 0.4)
   head(MSFT.SPLINE)
   plot(MSFT.SPLINE)
   title(main = "Close - Spline Smoothed")
   
## Plot Original and Smoothed Series by Supsmu - 
   MSFT.SUPSMU <- smoothSupsmu(MSFT.CLOSE)
   head(MSFT.SUPSMU)
   plot(MSFT.SUPSMU)
   title(main = "Close - Spline Smoothed")

Run the code above in your browser using DataLab