  Classification of time series signals has become an important construct and
has many practical applications. With existing classifiers we may be able to
accurately classify signals, however that accuracy may decline if using a
reduced number of attributes. Transforming the data then undertaking reduction
in dimensionality may improve the quality of the data analysis, decrease time
required for classification and simplify models. We propose an approach, which
chooses suitable wavelets to transform the data, then combines the output from
these transforms to construct a dataset to then apply ensemble classifiers to.
We demonstrate this on different data sets, across different classifiers and
use differing evaluation methods. Our experimental results demonstrate the
effectiveness of the proposed technique, compared to the approaches that use
either raw signal data or a single wavelet transform.
