PredictInterval

Description :
Evaluate Simplex prediction skill for forecast intervals from 1 to maxTp.

Python :

PredictInterval(dataFrame=None, columns='', target='',
lib='', pred='', maxTp=10, E=1, tau=-1, exclusionRadius=0,
embedded=False, validLib=[], noTime=False, ignoreNan=True,
verbose=False, numProcess=4, mpMethod=None, chunksize=1,
showPlot=True)

R :

PredictInterval(pathIn="./", dataFile="", dataFrame=NULL,
pathOut="./",  predictFile="", lib="", pred="",
maxTp=10, E=1,  tau=-1, exclusionRadius=0, columns="",
target="", embedded=FALSE, verbose=FALSE,
validLib=vector(), numThreads=4, showPlot=TRUE, noTime=FALSE)

Parameter	Type	Default	Purpose
dataFrame	pyEDM: pandas DataFrame rEDM: data.frame	None	Input DataFrame
columns	string or []	""	Column names for library
target	string	""	Prediction target column name
lib	string or []	""	Pairs of library start stop row indices
pred	string or []	""	Pairs of prediction start stop row indices
maxTp	int	10	Evaluate forecast with Tp up to maxTp
E	int	0	Embedding dimension
tau	int	-1	Embedding shift (time series rows)
exclusionRadius	int	0	Prediction vector exclusion radius
embedded	bool	False	Is data an embedding?
validLib	bool []	[] or None	Conditional embedding
noTime	bool	False	Do not require first data column of time or index
verbose	bool	False	Echo messages
numThreads	int	4	Number of threads to use
showPlot	bool	True	Show plot of E vs Rho (pyEDM, rEDM)
pathIn	string	"./"	Input data file path
dataFile	string	""	Data file name
mpMethod	string	None	multiprocessing context start method
chunksize	int	1	multiprocessing chunksize

Refer to the parameters table for general parameter definitions.

Notes :
Version 1.x: numThreads defines the number of worker threads for the maxTp embeddings.

Version 2.x: numProcess defines the number of processes for the maxTp embeddings.

Returns :
DataFrame with columns Tp and rho.