AMP: a new time-frequency feature extraction method for intermittent time-series data - Barrack, Duncan S.; Goulding, James; Hopcraft, Keith; Preston, Simon; Smith, Gavin (2015)
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.