  Time series classification has received great attention over the past decade
with a wide range of methods focusing on predictive performance by exploiting
various types of temporal features. Nonetheless, little emphasis has been
placed on interpretability and explainability. In this paper, we formulate the
novel problem of explainable time series tweaking, where, given a time series
and an opaque classifier that provides a particular classification decision for
the time series, we want to find the minimum number of changes to be performed
to the given time series so that the classifier changes its decision to another
class. We show that the problem is NP-hard, and focus on two instantiations of
the problem, which we refer to as reversible and irreversible time series
tweaking. The classifier under investigation is the random shapelet forest
classifier. Moreover, we propose two algorithmic solutions for the two problems
along with simple optimizations, as well as a baseline solution using the
nearest neighbor classifier. An extensive experimental evaluation on a variety
of real datasets demonstrates the usefulness and effectiveness of our problem
formulation and solutions.
