  Time series classification (TSC) aims to predict the class label of a given
time series, which is critical to a rich set of application areas such as
economics and medicine. State-of-the-art TSC methods have mostly focused on
classification accuracy and efficiency, without considering the
interpretability of their classifications, which is an important property
required by modern applications such as appliance modeling and legislation such
as the European General Data Protection Regulation. To address this gap, we
propose a novel TSC method - the Randomized-Supervised Time Series Forest
(r-STSF). r-STSF is highly efficient, achieves state-of-the-art classification
accuracy and enables interpretability. r-STSF takes an efficient interval-based
approach to classify time series according to aggregate values of
discriminatory sub-series (intervals). To achieve state-of-the-art accuracy,
r-STSF builds an ensemble of randomized trees using the discriminatory
sub-series. It uses four time series representations, nine aggregation
functions and a supervised binary-inspired search combined with a feature
ranking metric to identify highly discriminatory sub-series. The discriminatory
sub-series enable interpretable classifications. Experiments on extensive
datasets show that r-STSF achieves state-of-the-art accuracy while being orders
of magnitude faster than most existing TSC methods. It is the only classifier
from the state-of-the-art group that enables interpretability. Our findings
also highlight that r-STSF is the best TSC method when classifying complex time
series datasets.
