Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time series, as in the stock market to characterize price histories. In some analysis tasks, temporal information is complemented by other kinds of data, which may be represented by static attributes, e.g., categorical or numerical ones. J48SS is a novel decision tree inducer capable of natively mixing static (i.e., numerical and categorical), sequential, and time series data for classification purposes. The algorithm is based on the popular C4.5 decision tree learner, and it relies on the concepts of frequent pattern extraction and time series shapelet generation. The algorithm has been already applied text classification tasks in real world settings, as well as on a selection of public UCR time series datasets. Results showed that it is capable of providing competitive classification performances, while generating highly interpretable models and effectively reducing the data preparation effort.

This entry is adapted from the peer-reviewed paper 10.3390/computers8010021