Post Hoc Explainability for Time Series Classification: Toward a signal processing perspective

Time series data correspond to observations of phenomena that are recorded over time
[1]
. Such data are encountered regularly in a wide range of applications, such as speech and music recognition, monitoring health and medical diagnosis, financial analysis, motion tracking, and shape identification, to name a few. With such a diversity of applications and the large variations in their characteristics, time series classification is a complex and challenging task. One of the fundamental steps in the design of time series classifiers is that of defining or constructing the
discriminant features
that help differentiate between classes. This is typically achieved by designing novel
representation techniques

[2]
that transform the raw time series data to a new data domain, where subsequently a classifier is trained on the transformed data, such as one-nearest neighbors
[3]
or random forests
[4]
. In recent time series classification approaches, deep neural network models have been employed that are able to jointly learn a representation of time series and perform classification
[5]
. In many of these sophisticated approaches, the discriminant features tend to be complicated to analyze and interpret, given the high degree of nonlinearity.