Despite the popularisation of the machine learning models, more often than not they still operate as black boxes with no insight into what is happening inside the model. There exist a few methods that allow to visualise and explain why the model has made a certain prediction. Those methods, however, allow viewing the causal link between the input and output of the model without presenting how the model learns to represent the data. In this paper, a method that addresses that issue is proposed, with a focus on visualising multi-dimensional time-series data. Experiments on a high-frequency stock market dataset show that the method provides fast and discernible visualisations. Large datasets can be visualised quickly and on one plot, which makes it easy for a user to compare the learned representations of the data. The developed method successfully combines known and proven techniques to provide novel insight into the inner workings of time-series classifier models.

翻译：尽管机器学习模型已经普及,但它们往往仍然作为黑盒运作,对模型内正在发生的情况没有洞察力。有一些方法可以可视化和解释模型作出某种预测的原因。这些方法允许查看模型输入和输出之间的因果联系,而不必说明模型如何学会如何代表数据。本文提出了解决这一问题的方法,重点是可视化多维时间序列数据。高频股票市场数据集的实验显示,该方法提供了快速和可见的可视化。大数据集可以快速可视化,也可以在一个图中进行,使用户很容易比较所学的数据表述。开发的方法成功地结合了已知和经过验证的技术,为时间序列分类模型的内部工作提供了新的洞察力。