Mining patterns in discrete-valued time series databases

Discovering qualitative and quantitative patterns in temporal databases (that is, temporal data mining) is a challenging area of research. Discrete-valued time series (DTS) are common in practice in temporal databases and consist of a sequence of observations on particular variables over time. In this paper, we present a summary of the models that have been proposed in a variety of application areas. Then we present a new approach based on hidden periodicity analysis for mining similar and periodic patterns in a real-world temporal dataset.