Visual performance improvement analytics of predictive model for unbalanced panel data


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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This work was partly supported by Institute for Information & communication Technology Planning & Evaluation(IITP, Korea) funded by the Korea government(MSIT) (No. 2019-0-00795, Development of integrated cross-model data processing platform supporting a unified analysis of various big data models), (No. 2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation). Yun Jang is the corresponding author.

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Yeon, H., Son, H. & Jang, Y. Visual performance improvement analytics of predictive model for unbalanced panel data. J Vis (2021).

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  • Visual analytics
  • Model optimization
  • Incomplete data