@conference{Dictionary-Based_TimeSeries_2023,
author = "Rafael Ayll{\'o}n-Gavil{\'a}n and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "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.",
booktitle = "IWANN 2023: Advances in Computational Intelligence",
doi = "10.1007/978-3-031-43078-7_44",
keywords = "Time Series, Dictionary-Based Approaches, Ordinal Classification",
number = "1",
pages = "1--12",
title = "{A} {D}ictionary-{B}ased {A}pproach to {T}ime {S}eries {O}rdinal {C}lassification",
url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_44",
year = "2023",
}