  Time Series Classification (TSC) is an extensively researched field from
which a broad range of real-world problems can be addressed obtaining excellent
results. One sort of the approaches performing well are the so-called
dictionary-based techniques. The Temporal Dictionary Ensemble (TDE) is the
current state-of-the-art dictionary-based TSC approach. In many TSC problems we
find a natural ordering in the labels associated with the time series. This
characteristic is referred to as ordinality, and can be exploited to improve
the methods performance. The area dealing with ordinal time series is the Time
Series Ordinal Classification (TSOC) field, which is yet unexplored. In this
work, we present an ordinal adaptation of the TDE algorithm, known as ordinal
TDE (O-TDE). For this, a comprehensive comparison using a set of 18 TSOC
problems is performed. Experiments conducted show the improvement achieved by
the ordinal dictionary-based approach in comparison to four other existing
nominal dictionary-based techniques.
