Iterated Function System Models in Data Analysis: Detection and Separation

TL;DR

This paper explores the use of iterated function system models for analyzing data, developing an algorithm to detect regime switches, and demonstrating its application on computer performance data with potential uses in change-point detection and digital communications.

Contribution

The paper introduces a novel algorithm for detecting regime switches in IFS models, applicable to various data analysis tasks.

Findings

Successfully detected regime switches in a simple IFS

Applied the method to computer performance data

Potential for broad applications in time-series analysis

Abstract

We investigate the use of iterated function system (IFS) models for data analysis. An IFS is a discrete dynamical system in which each time step corresponds to the application of one of a finite collection of maps. The maps, which represent distinct dynamical regimes, may act in some pre-determined sequence or may be applied in random order. An algorithm is developed to detect the sequence of regime switches under the assumption of continuity. This method is tested on a simple IFS and applied to an experimental computer performance data set. This methodology has a wide range of potential uses: from change-point detection in time-series data to the field of digital communications.