Analysis of time series and signals using the Square Wave Method

TL;DR

This paper demonstrates the Square Wave Method as an effective mathematical tool for analyzing various time series and signals, showcasing its application on a large, randomly generated dataset.

Contribution

It introduces the Square Wave Method for time series analysis and applies it to a large, randomly generated dataset to illustrate its capabilities.

Findings

Successful analysis of a 10,000-value random time series

SWM effectively characterizes different types of signals

Potential for broad application in signal processing

Abstract

The Square Wave Method (SWM), previously introduced for the analysis of signals and images, is presented here as a mathematical tool suitable for the analysis of time series and signals. To show the potential that the SWM has to analyze many different types of time series, the results of the analysis of a time series composed of a sequence of 10,000 numerical values are presented here. These values were generated by using the Mathematical Random Number Generator (MRNG).