Multiview: Multiview embedding forecast

In rEDM: Empirical Dynamic Modeling ('EDM')

Multiview embedding forecast

Description

Multiview embedding forecast (Ye & Sugihara 2016): rank D-column views and average the best to find optimal combinations of variables that best represent the dynamics.

Usage

Multiview(dataFrame = NULL, columns, target, lib = numeric(0), pred = numeric(0),
          D = 0, E = 1, Tp = 1, knn = 0, tau = -1, multiview = 0,
          exclusionRadius = 0, trainLib = TRUE, excludeTarget = FALSE,
          ignoreNan = TRUE, numProcess = 4, backend = "RANN",
          pathIn = "./", dataFile = "", pathOut = "./", predictFile = "",
          parameterList = FALSE, showPlot = FALSE)

Arguments

dataFrame	A data.frame of input data. The first column is time unless noTime = TRUE.
columns	Column name(s) to build the embedding: character vector or space-separated string. If individual column names contain whitespace place names in a vector, or, append ',' to the name.
target	Target column name to predict.
lib	Library (training) index range as (start end) pairs.
pred	Prediction index range as (start end) pairs.
D	View dimension for Multiview (number of embedding columns combined).
E	Embedding dimension.
Tp	Forecast interval (prediction horizon).
knn	Number of nearest neighbours. 0 selects the method default.
tau	Embedding delay (negative selects past lags).
multiview	Number of top-ranked views to average (default floor(sqrt(nCombo))).
exclusionRadius	Temporal (Theiler) exclusion radius around each prediction point.
trainLib	Rank views on the library (in-sample) rather than the prediction set.
excludeTarget	Exclude the target variable from candidate views.
ignoreNan	Remove rows with NaN in the embedding from the library and prediction sets.
numProcess	Number of worker processes for the parameter sweep or task grid.
backend	Nearest-neighbour backend: "RANN" (default) or "brute".
pathIn	File path for input dataFile.
dataFile	Input dataFile, .csv format.
pathOut	Output file path for predictFile
predictFile	Output file name, .csv format.
parameterList	Append named list of parameters/values to return.
showPlot	If TRUE, draw a base-graphics plot of the result.

Details

Multiview embedding is a method to identify variables in a multivariate dynamical system that are most likely to contribute to the observed dynamics. It is a multistep algorithm with these general steps:

1. Compute D-dimensional variable combination forecasts.

2. Rank forecasts.

3. Compute predictions of top combinations.

4. Compute multiview averaged prediction.

If E>1, all variables are embedded to dimension E. If trainLib is TRUE initial forecasts and ranking are done in-sample (lib=pred) and predictions using the top ranked combinations use the specified lib and pred. If trainLib is FALSE initial forecasts and ranking use the specified lib and pred, the step of computing predictions of the top combinations is skipped.

Value

Named list with data.frames [[View, Predictions]].

data.frame View columns:

Col_1	column index
...	column index
Col_D	column index
rho	Pearson correlation
MAE	mean absolute error
RMSE	root mean square error
name_1	column name
...	column name
name_D	column name

If parameterList = TRUE a named list "parameters" is added.

References

Ye H., and G. Sugihara, 2016. Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science 353:922-925.

Examples

data(block_3sp)
L = Multiview( dataFrame = block_3sp, lib = "1 100", pred = "101 190",
E = 2, columns = "x_t y_t z_t", target = "x_t" )

rEDM documentation built on Aug. 30, 2026, 9:07 a.m.