fdaACF: fdaACF: Autocorrelation function for Functional Time Series
In fdaACF: Autocorrelation Function for Functional Time Series
Description
The fdaACF package provides diagnostic and analysis tools
to quantify the serial autocorrelation across lags of a
given functional time series in order to improve the identification
and diagnosis of functional ARIMA models. The autocorrelation function
is based on the L2 norm of the lagged covariance operators of the series.
Several real-world datasets are included to illustrate the application
of these techniques.
fdaACF documentation built on Oct. 23, 2020, 8:05 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
The fdaACF package provides diagnostic and analysis tools to quantify the serial autocorrelation across lags of a given functional time series in order to improve the identification and diagnosis of functional ARIMA models. The autocorrelation function is based on the L2 norm of the lagged covariance operators of the series. Several real-world datasets are included to illustrate the application of these techniques.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.