tde_gp {rEDM} | R Documentation

## Perform univariate forecasting using Gaussian processes

### Description

`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`.

### Usage

    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)
`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'

### Details

See `block_gp` for implementation details of the Gaussian process regression.

### Value

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

### 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)

[Package rEDM version 0.7.2 Index]