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.

Graphic abstract

中文翻译：

---

不平衡面板数据的预测模型的视觉性能改进分析

摘要

不平衡面板是一个数据集，其中有时至少没有观察到一个主题。而且，每个对象以不规则的周期和间隔记录。因此，数据中仅存在短趋势图样。当应用现有的预测技术时，创建反映单个主题模式的预测模型具有挑战性。此外，由于数据的总体趋势未知，因此预测结果中也存在不确定性。在本文中，我们提出了一种贝叶斯网络，可以根据不平衡的面板数据预测受试者的未来趋势。我们还提出了一种新方法来估计预测结果的预测间隔。此外，我们提出了一种视觉分析系统，该系统使我们能够从不平衡的面板数据构建预测模型。视觉分析系统还支持在已设计的预测模型中提高性能。我们根据各种数据模式在建立预测模型的同时评估系统的有效性。

图形摘要