predict.learnPattern: predict method for 'learnPattern' objects
In LPStimeSeries: Learned Pattern Similarity and Representation for Time Series

Description Usage Arguments Value Author(s) References See Also Examples

Representation generation for test data using learnPattern.

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## S3 method for class 'learnPattern'
predict(object, newdata, which.tree=NULL,
   nodes=TRUE, maxdepth=NULL, ...)

`object`	an object of class `learnPattern`, as that created by the function `learnPattern`.
`newdata`	a data frame or matrix containing new data.
`which.tree`	`NULL` if the representation is needed to be generated over all trees of ensemble. Set to an integer value if the representation is required to be generated for one tree specified by the value set.
`nodes`	`TRUE` generates the representation based on the trees. . `FALSE` generates a real-valued prediction for each time point.
`maxdepth`	The maximum depth level to generate the representation
`...`	not used currently.

Returns the learned pattern representation for the time series in the dataset if nodes is set TRUE. Basically, it is the count of observed patterns at each terminal node. Otherwise predicted values for each time series in newdata are returned.

Mustafa Gokce Baydogan

Baydogan, M. G. (2013), “Learned Pattern Similarity“, Homepage: http://www.mustafabaydogan.com/learned-pattern-similarity-lps.html.

Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.

learnPattern

data(GunPoint)
set.seed(71)
## Learn patterns on GunPoint training series with default parameters
ensemble=learnPattern(GunPoint$trainseries)

## Find representations
trainRep=predict(ensemble, GunPoint$trainseries, nodes=TRUE)
testRep=predict(ensemble, GunPoint$testseries, nodes=TRUE)

## Check size of the representation for training data
print(dim(trainRep))

## Learn patterns on GunPoint training series (target cannot be difference series)
ensemble=learnPattern(GunPoint$trainseries,target.diff=FALSE)

## Predict observations for test time series
predicted=predict(ensemble,GunPoint$testseries,nodes=FALSE)

## Plot an example test time series 
plot(GunPoint$testseries[5,],type='l',lty=1,xlab='Time',ylab='Observation',lwd=2)
points(c(1:ncol(GunPoint$testseries)),predicted$predictions[5,],type='l',col=2,lty=2,lwd=2)
legend('topleft',c('Original series','Approximation'),col=c(1,2),lty=c(1,2),lwd=2)