TY - JOUR
T1 - A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction
AU - Fujiwara, Takanori
AU - Shilpika,
AU - Sakamoto, Naohisa
AU - Nonaka, Jorji
AU - Yamamoto, Keiji
AU - Ma, Kwan Liu
N1 - Funding Information:
This research is sponsored in part by the U.S. National Science Foundation through grant IIS-1741536 and U.S. Department of Energy through grant DE-SC0014917.
PY - 2021/2
Y1 - 2021/2
N2 - Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data. However, DR is usually applied to a subset of data that is either single-time-point multivariate or univariate time-series, resulting in the need to manually examine and correlate the DR results out of different data subsets. When the number of dimensions is large either in terms of the number of time points or attributes, this manual task becomes too tedious and infeasible. In this paper, we present MulTiDR, a new DR framework that enables processing of time-dependent multivariate data as a whole to provide a comprehensive overview of the data. With the framework, we employ DR in two steps. When treating the instances, time points, and attributes of the data as a 3D array, the first DR step reduces the three axes of the array to two, and the second DR step visualizes the data in a lower-dimensional space. In addition, by coupling with a contrastive learning method and interactive visualizations, our framework enhances analysts' ability to interpret DR results. We demonstrate the effectiveness of our framework with four case studies using real-world datasets.
AB - Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data. However, DR is usually applied to a subset of data that is either single-time-point multivariate or univariate time-series, resulting in the need to manually examine and correlate the DR results out of different data subsets. When the number of dimensions is large either in terms of the number of time points or attributes, this manual task becomes too tedious and infeasible. In this paper, we present MulTiDR, a new DR framework that enables processing of time-dependent multivariate data as a whole to provide a comprehensive overview of the data. With the framework, we employ DR in two steps. When treating the instances, time points, and attributes of the data as a 3D array, the first DR step reduces the three axes of the array to two, and the second DR step visualizes the data in a lower-dimensional space. In addition, by coupling with a contrastive learning method and interactive visualizations, our framework enhances analysts' ability to interpret DR results. We demonstrate the effectiveness of our framework with four case studies using real-world datasets.
KW - data cube
KW - dimensionality reduction
KW - interpretability
KW - Multivariate time-series
KW - tensor
KW - visual analytics
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U2 - 10.1109/TVCG.2020.3028889
DO - 10.1109/TVCG.2020.3028889
M3 - Article
AN - SCOPUS:85100385023
VL - 27
SP - 1601
EP - 1611
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
IS - 2
M1 - 9216630
ER -