`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 specifies 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' |

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` |

) |

See `block_gp`

for implementation details of the Gaussian process
regression.

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