Last data update: 2014.03.03

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R Release (3.2.3)
CranContrib
BioConductor
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Results 1 - 10 of 36 found.
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var (Package: ftsa) : Variance

Generic functions for variance.
● Data Source: CranContrib
● Keywords: models
● Alias: var, var.default
● 0 images

ftsm (Package: ftsa) : Fit functional time series model

Fits a principal component model to a fts object. The function uses optimal orthonormal principal components obtained from a principal components decomposition.
● Data Source: CranContrib
● Keywords: models
● Alias: ftsm
● 0 images

quantile (Package: ftsa) : Quantile

Generic functions for quantile.
● Data Source: CranContrib
● Keywords: models
● Alias: quantile
● 0 images

plot.ftsm (Package: ftsa) : Plot fitted model components for a functional time series model

Plot showing the basis functions in the top row of plots and the coefficients in the bottom row of plots.
● Data Source: CranContrib
● Keywords: models
● Alias: plot.ftsm
● 0 images

summary.fm (Package: ftsa) : Summary for functional time series model

Summarizes a basis function model fitted to a functional time series. It returns various measures of goodness-of-fit.
● Data Source: CranContrib
● Keywords: models
● Alias: summary.fm
● 0 images

sd (Package: ftsa) : Standard deviation

Generic functions for standard deviation.
● Data Source: CranContrib
● Keywords: models
● Alias: sd, sd.default
● 0 images

quantile.fts (Package: ftsa) : Quantile functions for functional time series

Computes quantiles of functional time series at each variable.
● Data Source: CranContrib
● Keywords: methods
● Alias: quantile.fts
● 0 images

mean.fts (Package: ftsa) : Mean functions for functional time series

Computes mean of functional time series at each variable.
● Data Source: CranContrib
● Keywords: methods
● Alias: mean.fts
● 0 images

ftsmiterativeforecasts (Package: ftsa) : Forecast functional time series

The coefficients from the fitted object are forecasted using either an ARIMA model (method = "arima"), an AR model (method = "ar"), an exponential smoothing method (method = "ets"), a linear exponential smoothing method allowing missing values (method = "ets.na"), or a random walk with drift model (method = "rwdrift"). The forecast coefficients are then multiplied by the principal components to obtain a forecast curve.
● Data Source: CranContrib
● Keywords: models
● Alias: ftsmiterativeforecasts
● 0 images

pcscorebootstrapdata (Package: ftsa) :

Computes bootstrap or smoothed bootstrap samples based on either independent and identically distributed functional data or functional time series.
● Data Source: CranContrib
● Keywords: models
● Alias: pcscorebootstrapdata
● 0 images