tde_gp {rEDM} | R Documentation |
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, ...)
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) |
lib |
a 2-column matrix (or 2-element vector) where each row specifes the first and last *rows* of the time series to use for attractor reconstruction |
pred |
(same format as lib), but specifying the sections of the time series to forecast. |
E |
the embedding dimensions to use for time delay embedding |
tau |
the lag to use for time delay embedding |
tp |
the prediction horizon (how far ahead to forecast) |
phi |
length-scale parameter. see 'Details' |
v_e |
noise-variance parameter. see 'Details' |
eta |
signal-variance parameter. see 'Details' |
fit_params |
specify whether to use MLE to estimate params over the lib |
stats_only |
specify whether to output just the forecast statistics or the raw predictions for each run |
save_covariance_matrix |
specifies whether to include the full covariance matrix with the output (and forces the full output as if stats_only were set to FALSE) |
silent |
prevents warning messages from being printed to the R console |
... |
other parameters. see 'Details' |
See block_gp
for implementation details of the Gaussian process
regression.
If stats_only, then a data.frame with components for the parameters and forecast statistics:
E | embedding dimension |
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 |
If stats_only is FALSE or save_covariance_matrix is TRUE, then there is an additional list-column variable:
model_output | data.frame with columns for the time index, observations, and mean-value for predictions |
If save_covariance_matrix is TRUE, then there is an additional list-column variable:
covariance_matrix | covariance matrix for predictions |
data("two_species_model") ts <- two_species_model$x[1:200] tde_gp(ts, lib = c(1, 100), pred = c(101, 200), E = 5)