  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.
