Performance Examination of Symbolic Aggregate Approximation in IoT Applications

TL;DR

This paper evaluates the performance of the SAX algorithm for dimensionality reduction and shape recognition in IoT applications, focusing on computational efficiency and feature preservation.

Contribution

It introduces a SAX-based approach optimized for IoT platforms, addressing computational challenges in time-series analysis and shape recognition.

Findings

SAX can effectively reduce dimensionality while preserving shape features.

Performance varies with computational complexity of scenarios.

Proposed methods improve efficiency in IoT-like environments.

Abstract

Symbolic Aggregate approXimation (SAX) is a common dimensionality reduction approach for time-series data which has been employed in a variety of domains, including classification and anomaly detection in time-series data. Domains also include shape recognition where the shape outline is converted into time-series data forinstance epoch classification of archived arrowheads. In this paper we propose a dimensionality reduction and shape recognition approach based on the SAX algorithm, an application which requires responses on cost efficient, IoT-like, platforms. The challenge is largely dealing with the computational expense of the SAX algorithm in IoT-like applications, from simple time-series dimension reduction through shape recognition. The approach is based on lowering the dimensional space while capturing and preserving the most representative features of the shape. We present three scenarios of increasing computational complexity backing up our statements with measurement of performance characteristics