tde_gp is used in the same vein as simplex or s_map to 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 block_gp.

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, ...)

## Arguments

time_series 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'

## Value

If stats_only, then a data.frame with components for the parameters and forecast statistics:

 E embedding dimension tau time lag tp prediction horizon phi length-scale parameter v_e noise-variance parameter eta signal-variance parameter fit_params whether params were fitted or not num_pred number of predictions rho correlation coefficient between observations and predictions mae mean absolute error rmse root mean square error perc percent correct sign p_val p-value that rho is significantly greater than 0 using Fisher's z-transformation model_output data.frame with columns for the time index, observations, mean-value for predictions, and independent variance for predictions (if stats_only == FALSE or save_covariance_matrix == TRUE ) covariance_matrix the full covariance matrix for predictions (if save_covariance_matrix == TRUE )

## Details

See block_gp for implementation details of the Gaussian process regression.

## Examples

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