tde_gp is used in the same vein as
do time series forecasting using Gaussian processes. Here, the default
parameters are set so that passing a time series as the only argument will
run over E = 1:10 (embedding dimension) to created a lagged block, and
passing in that block and all remaining arguments into
tde_gp(time_series, lib = c(1, NROW(time_series)), pred = lib, E = 1:10, tau = 1, tp = 1, phi = 0, v_e = 0, eta = 0, fit_params = TRUE, stats_only = TRUE, save_covariance_matrix = FALSE, silent = FALSE, ...)
either a vector to be used as the time series, or a data.frame or matrix with at least 2 columns (in which case the first column will be used as the time index, and the second column as the time series)
a 2-column matrix (or 2-element vector) where each row specifies the first and last *rows* of the time series to use for attractor reconstruction
(same format as lib), but specifying the sections of the time series to forecast.
the embedding dimensions to use for time delay embedding
the lag to use for time delay embedding
the prediction horizon (how far ahead to forecast)
length-scale parameter. see 'Details'
noise-variance parameter. see 'Details'
signal-variance parameter. see 'Details'
specify whether to use MLE to estimate params over the lib
specify whether to output just the forecast statistics or the raw predictions for each run
specifies whether to include the full covariance matrix with the output (and forces the full output as if stats_only were set to FALSE)
prevents warning messages from being printed to the R console
other parameters. see 'Details'
If stats_only, then a data.frame with components for the parameters and forecast statistics:
|whether params were fitted or not|
|number of predictions|
|correlation coefficient between observations and||predictions|
|mean absolute error|
|root mean square error|
|percent correct sign|
|p-value that rho is significantly greater than 0 using|
|data.frame with columns for the time index,|
|observations, mean-value for predictions, and independent variance for||predictions (if|
|the full covariance matrix for predictions|
block_gp for implementation details of the Gaussian process
data("two_species_model") ts <- two_species_model$x[1:200] tde_gp(ts, lib = c(1, 100), pred = c(101, 200), E = 5)#> E tau embedding tp phi v_e eta fit_params num_pred #> 1 5 1 1, 2, 3, 4, 5 1 0.4597587 -5.685457 6.904747 TRUE 99 #> rho mae rmse perc p_val #> 1 0.9977515 0.01071626 0.01289583 1 6.917437e-243