Simplex

Description :
Simplex projection of the input data file or DataFrame.

Python :

Simplex(dataFrame=None, columns='', target='', 
lib='', pred='', E=0, Tp=1, knn=0, tau=-1, 
exclusionRadius=0, embedded=False, validLib=[], 
noTime = False, verbose=False, showPlot=False, 
ignoreNan = True, returnObject=False)

R :

Simplex(pathIn="./", dataFile="", dataFrame=NULL, 
pathOut="./", predictFile="", lib="", pred="", 
E=0, Tp=1, knn=0, tau=-1, exclusionRadius=0, 
columns="", target="", embedded=FALSE, 
const_pred=FALSE, verbose=FALSE, validLib=vector(), 
generateSteps=0, parameterList=FALSE, 
showPlot=FALSE, 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
E int 0 Data dimension
Tp int 1 Prediction Interval
knn int 0 Number nearest neighbors (if 0 then set to E+1)
tau int -1 Embedding time shift (time series rows)
exclusionRadius int 0 Prediction vector exclusion radius
embedded bool False Is data an embedding? If False, embed to E
verbose bool False Echo messages
showPlot bool False Plot results (pyEDM, rEDM)
validLib bool [] [] or None Condtional embedding
noTime bool False Do not require first data column of time or index
returnObject bool False pyEDM : return Simplex class object
generateSteps int 0 Number of recursive time step predictions
generateLibrary bool False Add generated data to library
parameterList bool False Include parameter dictionary in return
const_pred bool False Include non projected forecast data
pathIn string "./" Input data file path
dataFile string "" Data file name
pathOut string "./" Output file path
predictFile string "" Prediction output file


Refer to the parameters table for general parameter definitions.

Notes :
If embedded is false (default) the data columns are embedded to dimension E with time shift tau. If knn = 0, it is set to E+1.

Version 1.x : nan values are passed through all numeric computations in cppEDM. Any prediction row (pred) with nan will result in nan simplex prediction. Any library vector with a nan , whether in the observation, or from time delay embedding used as a nearest neighbor, will result in nan simplex prediction.

Version 2.x : nan values are removed from the data unless ignoreNan = True.

validLib implements conditional embedding (CE). It is a boolean vector the same length as the number of time series rows. A false entry means that the state-space vector derived from the corresponding time series row will not be included in the state-space library. See examples.

If generateSteps > 0 Simplex operates in feedback generative mode. The values of pred are over-riden to start at the end of the data. At each step one prediction is made, added to the columns data, a new time-delay embedded is created, and the cycle repeated for generateSteps. Feedback generation only operates on a univariate time series that is time-delay embedded. The columns and target variables must be the same.

Returns :
Version 1.x : If parameterList = False, (default) returns a DataFrame with 3 columns : "Time", "Observations", "Predictions". If parameterList = True, returns a list with predictions, parameters.

Version 2.x : Returns a DataFrame with 3 columns : "Time", "Observations", "Predictions". If returnObject = True returns the Simplex class object with all data and variables.