  The characterisation of time-series data via their most salient features is
extremely important in a range of machine learning task, not least of all with
regards to classification and clustering. While there exist many feature
extraction techniques suitable for non-intermittent time-series data, these
approaches are not always appropriate for intermittent time-series data, where
intermittency is characterized by constant values for large periods of time
punctuated by sharp and transient increases or decreases in value.
  Motivated by this, we present aggregation, mode decomposition and projection
(AMP) a feature extraction technique particularly suited to intermittent
time-series data which contain time-frequency patterns. For our method all
individual time-series within a set are combined to form a non-intermittent
aggregate. This is decomposed into a set of components which represent the
intrinsic time-frequency signals within the data set. Individual time-series
can then be fit to these components to obtain a set of numerical features that
represent their intrinsic time-frequency patterns. To demonstrate the
effectiveness of AMP, we evaluate against the real word task of clustering
intermittent time-series data. Using synthetically generated data we show that
a clustering approach which uses the features derived from AMP significantly
outperforms traditional clustering methods. Our technique is further
exemplified on a real world data set where AMP can be used to discover
groupings of individuals which correspond to real world sub-populations.
