Deep learning (DL) approaches are being increasingly used for time-series forecasting, with many efforts devoted to designing complex DL models. Recent studies have shown that the DL success is often attributed to effective data representations, fostering the fields of feature engineering and representation learning. However, automated approaches for feature learning are typically limited with respect to incorporating prior knowledge, identifying interactions among variables, and choosing evaluation metrics to ensure that the models are reliable. To improve on these limitations, this paper contributes a novel visual analytics framework, namely TimeTuner, designed to help analysts understand how model behaviors are associated with localized correlations, stationarity, and granularity of time-series representations. The system mainly consists of the following two-stage technique: We first leverage counterfactual explanations to connect the relationships among time-series representations, multivariate features and model predictions. Next, we design multiple coordinated views including a partition-based correlation matrix and juxtaposed bivariate stripes, and provide a set of interactions that allow users to step into the transformation selection process, navigate through the feature space, and reason the model performance. We instantiate TimeTuner with two transformation methods of smoothing and sampling, and demonstrate its applicability on real-world time-series forecasting of univariate sunspots and multivariate air pollutants. Feedback from domain experts indicates that our system can help characterize time-series representations and guide the feature engineering processes.

翻译：深度学习（DL）方法正越来越多地被应用于时间序列预测，大量研究致力于设计复杂的深度学习模型。近期研究表明，深度学习的成功常归因于有效的数据表示，这促进了特征工程和表示学习领域的发展。然而，自动化的特征学习方法在融入先验知识、识别变量间的交互作用以及选择评估指标以确保模型可靠性等方面通常存在局限性。为改进这些不足，本文提出一种新颖的可视分析框架TimeTuner，旨在帮助分析人员理解模型行为如何与时间序列表示的局部相关性、平稳性及粒度相关联。该系统主要由以下两阶段技术构成：首先，利用反事实解释将时间序列表示、多变量特征与模型预测之间的关联进行联结；其次，设计了包括基于分区的相关矩阵和并置双变量条纹在内的多协调视图，并提供一组交互方式，使用户能够参与变换选择过程、在特征空间中导航并推理模型性能。我们通过平滑和采样两种变换方法实例化TimeTuner，并在单变量太阳黑子与多变量空气污染物这两种真实世界时间序列预测任务中展示其适用性。领域专家的反馈表明，我们的系统能够帮助表征时间序列表示并指导特征工程流程。