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，深度学习的成功往往归因于有效的数据表示，这推动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、识别变量间交互以及选择评估指标以确保模型可靠性方面通常存在局限性。为改进这些不足，本文提出一种名为TimeTuner的新型可视化分析框架，旨在帮助分析师理解模型行为如何与时间序列表示的局部相关性、平稳性和粒度相关联。该系统主要包含以下两阶段技术：首先，我们利用反事实解释来连接时间序列表示、多变量特征与模型预测之间的关系。其次，我们设计了多种协同视图（包括基于分区的相关性矩阵和并置双变量条纹图），并提供一系列交互操作，使用户能够介入变换选择过程、探索特征空间并推理模型性能。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及多变量空气污染物等真实世界时间序列预测中验证其适用性。领域专家反馈表明，本系统有助于刻画时间序列表示特征并指导特征工程流程。