Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series applications. These factors make it difficult for users, particularly those without domain knowledge, to comprehend AI model decisions and obtain meaningful explanations. We propose CGS-Mask, a post-hoc and model-agnostic cellular genetic strip mask-based saliency approach to address these challenges. CGS-Mask uses consecutive time steps as a cohesive entity to evaluate the impact of features on the final prediction, providing binary and sustained feature importance scores over time. Our algorithm optimizes the mask population iteratively to obtain the optimal mask in a reasonable time. We evaluated CGS-Mask on synthetic and real-world datasets, and it outperformed state-of-the-art methods in elucidating the importance of features over time. According to our pilot user study via a questionnaire survey, CGS-Mask is the most effective approach in presenting easily understandable time series prediction results, enabling users to comprehend the decision-making process of AI models with ease.

翻译：人工智能在时间序列预测领域具有巨大潜力，但大多数可解释工具在系统理解特征随时间的重要性方面能力有限。这些工具通常仅评估单个时间点，忽视输入的时间顺序，且忽略时间序列应用对时间敏感的特性。这使得用户——尤其是缺乏领域知识的用户——难以理解AI模型的决策并获得有意义的解释。我们提出CGS-Mask，一种基于细胞遗传条带掩码的事后且模型无关的显著性方法，以应对这些挑战。CGS-Mask将连续时间步视为一个整体单元，评估特征对最终预测的影响，提供随时间变化的二元和持续性特征重要性得分。我们的算法通过迭代优化掩码种群，在合理时间内获得最优掩码。我们在合成和真实数据集上评估了CGS-Mask，其在阐明特征随时间重要性方面优于现有最先进方法。根据通过问卷调查进行的初步用户研究，CGS-Mask在呈现易于理解的时间序列预测结果方面最为有效，使用户能轻松理解AI模型的决策过程。