论文标题
系列显着性：多元时间序列的时间解释预测
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
论文作者
论文摘要
时间序列预测是一项重要但具有挑战性的任务。尽管最近开发了深度学习方法来给出卓越的预测结果，但对于提高时间序列模型的解释性至关重要。先前的解释方法，包括通用神经网络和基于注意力的方法的方法，主要考虑在特征维度中的解释，同时忽略关键的时间维度。在本文中，我们介绍了用于多元时间序列的时间解释的系列显着框架预测，该框架考虑了特征和时间维度的预测解释。通过从时间序列的滑动窗口中提取“串联图像”，我们按照最小的破坏区域原理应用显着图分段。该系列显着性框架可以用于任何定义明确的深度学习模型，并作为数据增强功能，以获得更准确的预测。几个真实数据集的实验结果表明，我们的框架为时间序列预测任务生成时间解释，同时产生准确的时间序列预测。
Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models. Previous interpretation methods, including the methods for general neural networks and attention-based methods, mainly consider the interpretation in the feature dimension while ignoring the crucial temporal dimension. In this paper, we present the series saliency framework for temporal interpretation for multivariate time series forecasting, which considers the forecasting interpretation in both feature and temporal dimensions. By extracting the "series images" from the sliding windows of the time series, we apply the saliency map segmentation following the smallest destroying region principle. The series saliency framework can be employed to any well-defined deep learning models and works as a data augmentation to get more accurate forecasts. Experimental results on several real datasets demonstrate that our framework generates temporal interpretations for the time series forecasting task while produces accurate time series forecast.
