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 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 \({\mathbf {NP}}\)-hard, and focus on three instantiations of the problem using global and local transformations. In the former case, we investigate the k-nearest neighbor classifier and provide an algorithmic solution to the global time series tweaking problem. In the latter case, we investigate the random shapelet forest classifier and focus on two instantiations of the local time series tweaking problem, which we refer to as reversible and irreversible time series tweaking, and propose two algorithmic solutions for the two problems along with simple optimizations. An extensive experimental evaluation on a variety of real datasets demonstrates the usefulness and effectiveness of our problem formulation and solutions.

中文翻译：

---

本地和全球可解释的时间序列调整

在过去的十年中，时间序列分类已受到广泛关注，其通过利用各种类型的时间特征来关注预测性能的方法广泛。尽管如此，很少强调可解释性和可解释性。在本文中，我们提出了可解释的时间序列调整的新问题，其中，给定时间序列和为时间序列提供特定分类决策的不透明分类器，我们希望找到要对给定时间序列执行的更改以便分类器将其决定更改为另一个类。我们证明问题是\（{\ mathbf {NP}} \）- hard，并集中讨论使用全局和局部问题的三个实例转变。在前一种情况下，我们研究了k最近邻分类器，并为全局时间序列调整问题提供了一种算法解决方案。在后一种情况下，我们研究随机的shapelet森林分类器，并专注于局部时间序列调整问题的两个实例化，我们将它们称为可逆和不可逆时间序列调整，并针对这两个问题提出了两种算法解决方案以及简单的优化方法。对各种真实数据集的广泛实验评估证明了我们提出问题和解决方案的有用性和有效性。