# Nested Pattern Detection and Unidimensional Process Characterization

## TL;DR

This paper introduces a new method for analyzing long texts by identifying repeating patterns and structures, useful for understanding complex processes.

## Contribution

The novel contribution is the Nested Repeated Sequence Decomposition Model (NRSDM) for detecting nested patterns in unidimensional processes.

## Key findings

- NRSDM effectively identifies nested patterns in non-periodic and chaotic processes.
- The model outperforms classical Fourier transform in analyzing oscillatory but non-periodic data.
- Graphical representations enhance the characterization of unidimensional processes.

## Abstract

This document introduces methods for describing long texts as groups of repeating symbols or patterns. The process converts a series of real-number values into texts. Developed tailored algorithms for identifying repeated sequences in the text are applied to decompose the text into nested tree-like structures of repeating symbols and is called the Nested Repeated Sequence Decomposition Model (NRSDM). The NRSDM is especially valuable for extracting repetitive behaviors in oscillatory but non-periodic and chaotic processes where the classical Fourier transform has limited application. The NRSDM along with the two graphical representations proposed here form a promising tool for characterizing long texts configured to represent the behavior of unidimensional processes.