The Extended Edit Distance Metric

TL;DR

This paper introduces a new metric for symbolic data similarity, specifically applied to time series classification, demonstrating its metric properties and comparing its performance with existing distances.

Contribution

A novel distance metric for symbolic data objects that is mathematically proven to be a metric and tested on time series classification tasks.

Findings

The proposed distance is mathematically proven to be a metric.

It performs competitively in time series classification.

Compared to existing distances, it offers a new approach for symbolic data similarity.

Abstract

Similarity search is an important problem in information retrieval. This similarity is based on a distance. Symbolic representation of time series has attracted many researchers recently, since it reduces the dimensionality of these high dimensional data objects. We propose a new distance metric that is applied to symbolic data objects and we test it on time series data bases in a classification task. We compare it to other distances that are well known in the literature for symbolic data objects. We also prove, mathematically, that our distance is metric.