View source: R/LegacyInterface.R

block_lnlp | R Documentation |

`block_lnlp`

uses multiple time series given as input to generate
an attractor reconstruction, and then applies the simplex projection or
s-map algorithm to make forecasts. This method generalizes the
`simplex`

and `s_map`

routines, and allows for
"mixed" embeddings, where multiple time series can be used as different
dimensions of an attractor reconstruction.

```
block_lnlp(block, lib = NULL, pred = NULL, norm = 2, method = c("simplex",
"s-map"), tp = 1, num_neighbors = switch(match.arg(method),
simplex = "e+1", `s-map` = 0), columns = NULL, target_column = 1,
stats_only = TRUE, first_column_time = FALSE, exclusion_radius = NULL,
epsilon = NULL, theta = NULL, silent = TRUE, save_smap_coefficients = FALSE)
```

`block` |
either a vector to be used as the time series, or a data.frame or matrix where each column is a time series |

`lib` |
a 2-column matrix, data.frame, 2-element vector or string of row indice pairs, where each pair specifies the first and last *rows* of the time series to create the library. If not specified, all available rows are used |

`pred` |
(same format as lib), but specifying the sections of the time series to forecast. If not specified, set equal to lib |

`norm` |
the distance measure to use. see 'Details' |

`method` |
the prediction method to use. see 'Details' |

`tp` |
the prediction horizon (how far ahead to forecast) |

`num_neighbors` |
the number of nearest neighbors to use. Note that the default value will change depending on the method selected. (any of "e+1", "E+1", "e + 1", "E + 1" will set this parameter to E+1 for each run.) |

`columns` |
either a vector with the columns to use (indices or names), or a list of such columns |

`target_column` |
the index (or name) of the column to forecast |

`stats_only` |
specify whether to output just the forecast statistics or to include the raw predictions for each run |

`first_column_time` |
indicates whether the first column of the given block is a time column (and therefore excluded when building the library) |

`exclusion_radius` |
excludes vectors from the search space of nearest neighbors if their *time index* is within exclusion_radius (NULL turns this option off) |

`epsilon` |
Not implemented |

`theta` |
the nonlinear tuning parameter (theta is only relevant if method == "s-map") |

`silent` |
prevents warning messages from being printed to the R console |

`save_smap_coefficients` |
specifies whether to include the s_map coefficients with the output |

The default parameters are set so that passing a vector as the only argument will use that vector to predict itself one time step ahead. If a matrix or data.frame is given as the only argument, the first column will be predicted (one time step ahead), using the remaining columns as the embedding. If the first column is not a time vector, 1:NROW will be used as time values.

`norm = 2`

(only option currently available) uses the "L2 norm",
Euclidean distance:

```
distance(a,b) := \sqrt{\sum_i{(a_i - b_i)^2}}
```

method "simplex" (default) uses the simplex projection forecasting algorithm

method "s-map" uses the s-map forecasting algorithm

A data.frame with components for the parameters and forecast statistics:

cols | embedding |

tp | prediction horizon |

nn | number of neighbors |

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 |

const_pred_rho | same as `rho` , but for the constant predictor |

const_pred_mae | same as `mae` , but for the constant predictor |

const_pred_rmse | same as `rmse` , but for the constant predictor |

const_pred_perc | same as `perc` , but for the constant predictor |

const_p_val | same as `p_val` , but for the constant predictor |

model_output | data.frame with columns for the time index,
observations, predictions, and estimated prediction variance
(if `stats_only == FALSE` ) |

If "s-map" is the method, then the same, but with additional columns:

theta | the nonlinear tuning parameter |

smap_coefficients | data.frame with columns for the s-map
coefficients (if `save_smap_coefficients == TRUE` ) |

smap_coefficient_covariances | list of covariance matrices for
the s-map coefficients (if `save_smap_coefficients == TRUE` ) |

```
block <- block_3sp
block_lnlp(block[,2:4])
block <- block_3sp
block_lnlp(block[,1:4], first_column_time = TRUE)
block <- block_3sp
block_lnlp(block, target_column = "x_t", columns = c("y_t", "z_t"), first_column_time = TRUE)
block <- block_3sp
x_t_pred = block_lnlp(block, columns = c("x_t", "y_t"), first_column_time = TRUE,
stats_only = FALSE)
block <- block_3sp
x_t_pred = block_lnlp(block, method = "s-map", theta = 3, columns =
c("x_t", "y_t"), first_column_time = TRUE, stats_only = FALSE, save_smap_coefficients = TRUE)
```

rEDM documentation built on July 9, 2023, 5:11 p.m.

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