Simplex
Description :
Simplex projection of the input data file or DataFrame.
Python :
Simplex(pathIn='./', dataFile='', dataFrame=None, pathOut='./',
predictFile='', lib='', pred='', E=0, Tp=1, knn=0, tau=-1,
exclusionRadius=0, columns='', target='', embedded=False,
verbose=False, const_pred=False, showPlot=False, validLib=[],
generateSteps=0, generateLibrary=False, parameterList=False,
noTime=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 |
|---|---|---|---|
| pathIn | string | "./" | Input data file path |
| dataFile | string | "" | Data file name |
| dataFrame | pyEDM: pandas DataFrame rEDM: data.frame |
None | Input DataFrame |
| pathOut | string | "./" | Output file path |
| predictFile | string | "" | Prediction output file |
| 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 |
| columns | string or [] | "" | Column names for library |
| target | string | "" | Prediction target column name |
| embedded | bool | False | Is data an embedding? If False, embed to E |
| verbose | bool | False | Echo messages |
| const_pred | bool | False | Include non projected forecast data |
| showPlot | bool | False | Plot results (pyEDM, rEDM) |
| validLib | bool [] | [] or None | Condtional embedding |
| 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 |
| noTime | bool | False | Do not require first data column of time or index |
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.
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.
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 :
If parameterList = False, (default) the returned object is a DataFrame with 3 columns : "Time", "Observations", "Predictions".
If parameterList = True, a dictionary with keys predictions, parameters is returned. Dictionary values are the predictions DataFrame and parameter dictionary respectively.