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Erschienen in: Journal of Visualization 3/2021

03.01.2021 | Regular Paper

Visual performance improvement analytics of predictive model for unbalanced panel data

verfasst von: Hanbyul Yeon, Hyesook Son, Yun Jang

Erschienen in: Journal of Visualization | Ausgabe 3/2021

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Abstract

An unbalanced panel is a dataset in which at least one subject is not observed some times. Moreover, each subject is recorded with irregular periods and intervals. Therefore, only short trend pattern pieces exist in the data. When applying existing prediction techniques, it is challenging to create a prediction model that reflects individual subject patterns. Also, uncertainties in the predicted results emerge since the overall trend of the data is unknown. In this paper, we present a Bayesian network to predict the future trends of subjects from the unbalanced panel data. We also present a new approach to estimate the predicted intervals of the predicted results. Moreover, we propose a visual analytics system that enables us to build a prediction model from unbalanced panel data. The visual analytics system also supports performance improvement in the already designed prediction model. We evaluate the effectiveness of our system while building a predictive model according to various data patterns.

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Metadaten
Titel
Visual performance improvement analytics of predictive model for unbalanced panel data
verfasst von
Hanbyul Yeon
Hyesook Son
Yun Jang
Publikationsdatum
03.01.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Visualization / Ausgabe 3/2021
Print ISSN: 1343-8875
Elektronische ISSN: 1875-8975
DOI
https://doi.org/10.1007/s12650-020-00716-0

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