Given a timeseries (timeseries) and another time series
of categorical values (groups) the makewpstDO produces
a model that permits discrimination of the groups series using
a discriminant analysis based on a restricted set of non-decimated
wavelet packet coefficients of timeseries. The current function
enables new timeseries data, to be used in conjunction with
the model to generate new, predicted, values of the groups time series.
wpstCLASS(newTS, wpstDO)The prediction using the usual R predict.lda function. The
predicted values are stored in the class component of that list.
A new segment of time series values, of the same time series that was used as the dependent variable used to construct the wpstDO object
An object that uses values of a dependent time series to
build a discriminatory model of a groups time series. Output
from the makewpstDO function
G P Nason
This function performs the same nondecimated wavelet packet (NDWPT) transform
of the newTS data that was used to analyse the original timeseries
and the details of this transform are stored within the wpstDO
object. Then, using information that was recorded in wpstDO
the packets with the same level/index are extracted from the new NDWPT
and formed into a matrix. Then the linear discriminant variables,
again stored in wpstDO are used to form predictors of the
original groups time series, ie new values of groups
that correspond to the new values of timeseries.
makewpstDO
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# See example at the end of help page for makewpstDO
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