An analytical framework for formulating metrics for evaluating multi-dimensional time-series data

This paper proposes a visual analytics framework for formulating metrics for evaluating multi-dimensional time-series data. Multidimensional time-series data has been collected and utilized in different domains. We believe evaluation metrics play an important role in utilizing those data, such as decision making and labeling training data used in machine learning. However, it is a difficult task for even domain experts to formulate metrics. To support the process of formulating metrics, the proposed framework represents metrics as a linear combination of data attributes, and provides a means for formulating it through interactive data exploration. A prototype interface that visualizes target data as an animated scatter plot was implemented. Through this interface, several visualized objects can be directly manipulated: a node and a trajectory of an instance, and a convex hull as the group of nodes and trajectories. Linear combinations of attributes are adjusted in accordance with the manipulation of different objects' types by the user. The effectiveness of the proposed framework was demonstrated through two application examples with real-world data.