Visual predictive modeling of incomplete time series panel data

Abstract

It is not easy to predict incomplete panel data whose overall trend is not complete. For example, physical growth data for age 7-18 has been collected every a few months for up to seven years; therefore, only short growth pattern pieces exist in the data. When using previous prediction techniques, it is challenging to create a growth prediction model that reflects individual growth patterns. Also, uncertainties in predicted results emerge since the overall trend of the data is unknown. In this work, we present a predictive analysis and modeling to forecast incomplete data over time. We extend the Bayesian network model, which is relative data-driven approaches that explore similar data and weave them to create approximate inference margins. Besides, we propose a visual analytics system that enables us to design various predictive models that reflect individual growth pattern. Our visual analytics system assists us to discover new growth patterns in the process of analyzing the accuracy of previously designed predictive models. Moreover, the system allows us to optimize predictive models to fit unusual growth patterns.