Parameters

dataFrame

Input data.frame. The columns must be named.
The first column must be time index, strings or values unless noTime = True.

columns

String or vector of column name(s) in the input data used to create the library. String names must be whitespace separated.

target

String of column name in the input data used for prediction.

lib

String or vector with pairs of start and stop indices of input data rows used to create the library of E-dimensional embedding vectors. String indices must be whitespace separated.

pred

String or vector with pairs of start and stop indices of input data rows
where predictions are made. String indices must be whitespace separated.

Tp

Prediction horizon (number of time column rows).

E

Embedding dimension.

tau

Time offset of embedding specified as number of time series rows. tau < 0 are lags, tau > 0 future values.

theta

In Smap: S-Map neighbor localisation exponent. Single numeric.
In PredictNonlinear: A whitespace delimeted string or numeric vector with values of S-map localisation parameters to be evaluated.

knn

Number of nearest neighbors. If knn=0; knn is set to E+1 for Simplex(); set to number of lib data rows for SMap().

exclusionRadius

Excludes library vectors from the search space of nearest neighbors if their relative time index is within exclusionRadius.

embedded

Logical specifying if the input data are embedded. If embedded = True, no emedding is performed. If embedded = False, all input data are embedded to dimension E with time shift tau.

libSizes

String or vector of integers specifying CCM library sizes. If three values are provided, and, if the third value is less than the second, they are treated as a sequence generator specifying the intial library size; the final library size; and the library size increment. String values must be whitespace separated.

sample

Integer specifying the number of random samples to draw at each library size evaluation for CCM.

random

Logical to specify random (True) or sequential library sampling (False) in CCM.

replacement

Logical to specify sampling with replacement in CCM. Not recommended.

includeData

Logical to return all CCM projection data frames.

seed

Integer specifying the random sampler seed in CCM. If seed=0, then a random seed is generated.

multiview

Number of multiview ensembles to average for the final prediction estimate in Multiview.

D

Multiview dimension.

trainLib

Use in-sample (lib=pred) prediction for multiview ranking

excludeTarget

Exclude target variable from multiviews

maxE

Maximum value of E to evalulate in EmbedDimension.

maxTp

Maximum value of Tp to evalulate in PredictInterval.

numThreads

Number of parallel threads for computation in EmbedDimension; PredictInterval and PredictNonlinear.

pathIn

Filesystem path to input dataFile. CSV format.

dataFile

CSV format data file name. The first row must be column names.
The first column must be time index, strings or values unless noTime = True.

pathOut

Filesystem path for predictFile containing output predictions.

predictFile

Observation and Prediction output file name. CSV format.

smapCoefFileSMap

Coefficient output file name. CSV format.

smapSVFile

SMap singular value output file name. CSV format.

solver

In pyEDM SMap(): An instance of a sklearn.linear_model object.

ignoreNan

Logical specifying whether data NaN values are to be ignored in SMap: default True.

noTime

Logical indicating whether input data lack a time vector in first column: default False.

validLib

Conditional embedding. Boolean vector identifying time series rows whose corresponding embedding vectors define the state-space library.

generateSteps

Generative feedback predictions for Simplex() or SMap().

parameterList

Add parameter dictionary to return objects in Simplex(),SMap(),CCM(),Multiview().

verbose

Logical to produce additional console reporting.

const_pred

Logical to add a constant predictor column to the output. The constant predictor is X(t+1) = X(t).

showPlot

Logical to plot results (pyEDM & rEDM).