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The characterisation of time-series data via their most salient features isextremely important in a range of machine learning task, not least of all withregards to classification and clustering. While there exist many featureextraction techniques suitable for non-intermittent time-series data, theseapproaches are not always appropriate for intermittent time-series data, whereintermittency is characterized by constant values for large periods of timepunctuated 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 intermittenttime-series data which contain time-frequency patterns. For our method allindividual time-series within a set are combined to form a non-intermittentaggregate. This is decomposed into a set of components which represent theintrinsic time-frequency signals within the data set. Individual time-seriescan then be fit to these components to obtain a set of numerical features thatrepresent their intrinsic time-frequency patterns. To demonstrate theeffectiveness of AMP, we evaluate against the real word task of clusteringintermittent time-series data. Using synthetically generated data we show thata clustering approach which uses the features derived from AMP significantlyoutperforms traditional clustering methods. Our technique is furtherexemplified on a real world data set where AMP can be used to discovergroupings of individuals which correspond to real world sub-populations.

AMP: a new time-frequency feature extraction method for intermittent time-series data