An Investigation of Time Series Embeddings for Topological Data Analysis

Abstract

Topological data analysis (TDA) is an important part of a data scientist's toolbox; through the extraction of topological information, TDA provides an automated means of feature engineering. Thus, TDA may be an appropriate tool for fault analysis of mechanical systems, where the methodologies have traditionally relied on expert intuition and signals processing techniques. However, appropriate embedding of the data must be defined before TDA can be applied. In this report, we investigate several different embeddings of time series data for the application of TDA for fault analysis,and show that features engineered by TDA improve fault classification accuracy.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2023
Accession Number
AD1215100

Entities

People

  • Andrew Sabater
  • Dean Lee
  • Jamal Rorie

Organizations

  • Naval Information Warfare Center Pacific

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Classification
  • Computer Science
  • Data Analysis
  • Data Mining
  • Data Preprocessing
  • Data Science
  • Data Sets
  • Deep Learning
  • Dimensionality Reduction
  • Embedding
  • Engineering
  • Extraction
  • Feature Extraction
  • Information Operations
  • Information Warfare
  • Machine Learning
  • Network Science
  • Signal Processing

Fields of Study

  • Computer science
  • Engineering

Readers

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