Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation

Muhammad Marwan Muhammad Fuad (1)

1 IDI - Department of Computer and Information Science [Trondheim]

Bio-inspired optimization algorithms have been gaining more popularity recently. One of the most important of these algorithms is particle swarm optimization (PSO). PSO is based on the collective intelligence of a swam of particles. Each particle explores a part of the search space looking for the optimal position and adjusts its position according to two factors; the first is its own experience and the second is the collective experience of the whole swarm. PSO has been successfully used to solve many optimization problems. In this work we use PSO to improve the performance of a well-known representation method of time series data which is the symbolic aggregate approximation (SAX). As with other time series representation methods, SAX results in loss of information when applied to represent time series. In this paper we use PSO to propose a new minimum distance WMD for SAX to remedy this problem. Unlike the original minimum distance, the new distance sets different weights to different segments of the time series according to their information content. This weighted minimum distance enhances the performance of SAX as we show through experiments using different time series datasets

Mots clés: Particle Swarm Optimization; Bio-inspired Optimization; Time Series Data Mining; Information Loss; Information Content; Symbolic Aggregate Approximation

Domaines: Recherche d'information [cs.IR]

Muhammad Marwan Muhammad Fuad. Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation. 8th International Conference, ADMA 2012, Dec 2012, Nanjing, China. pp.443-455, ⟨10.1007/978-3-642-35527-1_37⟩. ⟨hal-00846598⟩