Make forecasts using gaussian process regression

gausspr_ts fits a timeseries model using gausspr

gausspr_ts(timeseries, num_ahead = 5, level = 95, frequency = 1,
  kernel = "rbfdot", kpar = "automatic")

Arguments

timeseries	the time series to forecast
num_ahead	the number of points at the end of the time series to forecast
level	Confidence level for prediction intervals.
frequency	the number of observations per unit of time.
kernel	the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a dot product between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: rbfdot Radial Basis kernel function "Gaussian" polydot Polynomial kernel function vanilladot Linear kernel function tanhdot Hyperbolic tangent kernel function laplacedot Laplacian kernel function besseldot Bessel kernel function anovadot ANOVA RBF kernel function splinedot Spline kernel The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.
kpar	the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are : sigma inverse kernel width for the Radial Basis kernel function "rbfdot" and the Laplacian kernel "laplacedot". degree, scale, offset for the Polynomial kernel "polydot" scale, offset for the Hyperbolic tangent kernel function "tanhdot" sigma, order, degree for the Bessel kernel "besseldot". sigma, degree for the ANOVA kernel "anovadot". Hyper-parameters for user defined kernels can be passed through the kpar parameter as well.

Value

a data.frame of the mean forecasts, the observed values, and the lower and upper CI levels (if an error occurs, then just NA values)