SMap | R Documentation |

`SMap`

performs time series forecasting based on localised
(or global) nearest neighbor projection in the time series phase space as
described in Sugihara 1994.

SMap(pathIn = "./", dataFile = "", dataFrame = NULL, lib = "", pred = "", E = 0, Tp = 1, knn = 0, tau = -1, theta = 0, exclusionRadius = 0, columns = "", target = "", smapFile = "", embedded = FALSE, const_pred = FALSE, verbose = FALSE, validLib = vector(), generateSteps = 0, parameterList = FALSE, showPlot = FALSE)

`pathIn` |
path to |

`dataFile` |
.csv format data file name. The first column must be a time index or time values. The first row must be column names. |

`dataFrame` |
input data.frame. The first column must be a time index or time values. The columns must be named. |

`lib` |
string with start and stop indices of input data rows used to create the library of observations. A single contiguous range is supported. |

`pred` |
string with start and stop indices of input data rows used for predictions. A single contiguous range is supported. |

`E` |
embedding dimension. |

`Tp` |
prediction horizon (number of time column rows). |

`knn` |
number of nearest neighbors. If knn=0, knn is set to the library size. |

`tau` |
lag of time delay embedding specified as number of time column rows. |

`theta` |
neighbor localisation exponent. |

`exclusionRadius` |
excludes vectors from the search space of nearest neighbors if their relative time index is within exclusionRadius. |

`columns` |
string of whitespace separated column name(s) in the input data used to create the library. |

`target` |
column name in the input data used for prediction. |

`smapFile` |
output file containing SMap coefficients. |

`embedded` |
logical specifying if the input data are embedded. |

`const_pred` |
logical to add a |

`verbose` |
logical to produce additional console reporting. |

`validLib` |
logical vector the same length as the number of data rows. Any data row represented in this vector as FALSE, will not be included in the library. |

`generateSteps` |
number of predictive feedback generative steps. |

`parameterList` |
logical to add list of invoked parameters. |

`showPlot` |
logical to plot results. |

If `embedded`

is `FALSE`

, the data `column(s)`

are embedded
to dimension `E`

with time lag `tau`

. This embedding forms an
n-columns * E-dimensional phase space for the `SMap`

projection.
If embedded is `TRUE`

, the data are assumed to contain an
E-dimensional embedding with E equal to the number of `columns`

.
See the Note below for proper use of multivariate data (number of
`columns`

> 1).

Predictions are made using leave-one-out cross-validation, i.e. observation rows are excluded from the prediction regression.

In contrast to `Simplex`

, `SMap`

uses all
available neighbors and weights them with an exponential decay
in phase space distance with exponent `theta`

. `theta`

=0
uses all neighbors corresponding to a global autoregressive model.
As `theta`

increases, neighbors closer in vicinity to the
observation are considered.

A named list with two data.frames `[[predictions, coefficients]]`

.
`predictions`

has columns `Observations, Predictions`

. If
`const_pred`

is TRUE the column `Const_Predictions`

is added.
The first column contains time values.

`coefficients`

data.frame has time values in the first column.
Columns 2 through E+2 (E+1 columns) are the SMap coefficients.

If `parameterList = TRUE`

a named list "parameters" is added.

`SMap`

should be called with columns explicitly corresponding to
dimensions E. In the univariate case (number of `columns`

= 1) with
default `embedded = FALSE`

, the time series will be time-delay
embedded to dimension E, SMap coefficients correspond to each dimension.

If a multivariate data set is used (number of `columns`

> 1) it
must use `embedded = TRUE`

with E equal to the number of columns.
This prevents the function from internally time-delay embedding the
multiple columns to dimension E. If the internal time-delay embedding
is performed, then state-space columns will not correspond to the
intended dimensions in the matrix inversion, coefficient assignment,
and prediction. In the multivariate case, the user should first prepare
the embedding (using `Embed`

for time-delay embedding), then
pass this embedding to `SMap`

with appropriately specified
`columns`

, `E`

, and `embedded = TRUE`

.

Sugihara G. 1994. Nonlinear forecasting for the classification of natural time series. Philosophical Transactions: Physical Sciences and Engineering, 348 (1688):477-495.

data(circle) L = SMap( dataFrame = circle,lib="1 100", pred="110 190", theta = 4, E = 2, embedded = TRUE, columns = "x y", target = "x" )

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