Querying and mining of time series data

TLDR

Interest in querying and mining time‑series data has surged, with many dimensionality‑reduction and similarity‑measure methods proposed, yet comparative studies have largely focused narrowly on comparing new methods to a few predecessors. The study aims to comprehensively validate time‑series representation and similarity methods by re‑implementing eight representations and nine measures across 38 diverse datasets. The authors re‑implemented eight representation methods and nine similarity measures, evaluating them on 38 diverse time‑series datasets. The experiments confirm some reported advantages but also reveal that several claims in the literature are likely overstated.

Abstract

The last decade has witnessed a tremendous growths of interests in applications that deal with querying and mining of time series data. Numerous representation methods for dimensionality reduction and similarity measures geared towards time series have been introduced. Each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive set of time series experiments re-implementing 8 different representation methods and 9 similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this paper, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. Our experiments have provided both a unified validation of some of the existing achievements, and in some cases, suggested that certain claims in the literature may be unduly optimistic.