Grasping inter-attribute and temporal variability in multivariate time series

The rising capabilities of storing and registering data has increased the number of temporal datasets, boosting the attention on time series classification and forecasting. In case of multivariate time series, symbolic methods that try to predict phenomena transform the data into a more compact format to produce a representation of the time series easy to be handled in a machine learning framework. However, up to now these representations do not grasp information on both inter-attribute variability and temporal variability. In this work we present an approach that, taking into account the relationships between attributes and their periodicity, reduces the multivariate time series to a collection of symbols, whose distribution is represented by histograms. The approach has been successfully tested on a publicly available dataset, the Telecom Italia Big Data Challenge 2014 dataset, reporting also the results attained by other methods available in the literature.