Multi-period classification: learning sequent classes from temporal domains

As the majority of real-world decisions change over time, extending traditional classifiers to deal with the problem of classifying an attribute of interest across different time periods becomes increasingly important. Tackling this problem, referred to as multi-period classification, is critical to answer real-world tasks, such as the prediction of upcoming healthcare needs or administrative planning tasks. In this context, although existing research provides principles for learning single labels from complex data domains, less attention has been given to the problem of learning sequences of classes (symbolic time series). This work motivates the need for multi-period classifiers, and proposes a method, cluster-based multi-period classification (CMPC), that preserves local dependencies across the periods under classification. Evaluation against real-world datasets provides evidence of the relevance of multi-period classifiers, and shows the superior performance of the CMPC method against peer methods adapted from long-term prediction for multi-period tasks with a high number of periods.