Automatic Feature Extraction from Large Time Series

The classification of high dimensional data like time series requires the efficient extraction of meaningful features. The systematization of statistical methods allows automatic approaches to combine these methods and construct a method tree which delivers suitable features. It can be shown that the combination of efficient methods also works efficiently, which is especially necessary for the feature extraction from large value series. The transformation from raw series data to feature vectors is illustrated by different classification tasks in the domain of audio data.