Understanding how models process and interpret time series data remains a significant challenge in deep learning to enable applicability in safety-critical areas such as healthcare. In this paper, we introduce Sequence Dreaming, a technique that adapts Activation Maximization to analyze sequential information, aiming to enhance the interpretability of neural networks operating on univariate time series. By leveraging this method, we visualize the temporal dynamics and patterns most influential in model decision-making processes. To counteract the generation of unrealistic or excessively noisy sequences, we enhance Sequence Dreaming with a range of regularization techniques, including exponential smoothing. This approach ensures the production of sequences that more accurately reflect the critical features identified by the neural network. Our approach is tested on a time series classification dataset encompassing applications in predictive maintenance. The results show that our proposed Sequence Dreaming approach demonstrates targeted activation maximization for different use cases so that either centered class or border activation maximization can be generated. The results underscore the versatility of Sequence Dreaming in uncovering salient temporal features learned by neural networks, thereby advancing model transparency and trustworthiness in decision-critical domains.

翻译：理解模型如何处理和解释时间序列数据，仍然是深度学习在医疗等安全关键领域实现应用的重要挑战。本文提出序列幻构技术，该方法将激活最大化技术适配于序列信息分析，旨在提升处理单变量时间序列的神经网络的可解释性。通过运用此方法，我们可视化了在模型决策过程中最具影响力的时序动态与模式。为抑制生成不现实或过度噪声的序列，我们采用包括指数平滑在内的多种正则化技术增强序列幻构方法，确保生成的序列能更准确地反映神经网络识别的关键特征。我们在涵盖预测性维护应用的时间序列分类数据集上验证了该方法。结果表明，所提出的序列幻构方法能够针对不同用例实现定向激活最大化，既可生成中心类激活最大化，也可生成边界激活最大化。这些结果凸显了序列幻构技术在揭示神经网络学习到的显著时序特征方面的多功能性，从而提升了决策关键领域的模型透明度与可信度。