Comparison of Times Series Clustering via Dynamic Time Warping, Euclidean Distance, and Global Alignment Kernel

by John Akwei, ECMp ERMp Data Scientist

January 10, 2022

Table of Contents

Section 1 — Problem Definition
Section 1.1 — Project Summary

Section 2 — Data Preparation
Section 2.1 — Working Directory and Required Libraries
Section 2.2 — Import Data

Section 3 — Exploratory Data Analysis
Section 3.1 — Plot of Time Series Data
Section 3.2 — Dynamic Time Warping (DTW)

Section 4 — Model Development
Section 4.1 — Creating Distance Measure Models
Section 4.2 — Time Series Distance Measure Visualizations

Section 5 — Evaluation

Section 1 — Problem Definition

Compare methods of time series clustering with Dynamic Time Warping (DTW), Euclidean distance, and a third measure. Included in the Data Science analysis: Required R Language Libraries, Data Importation, Exploratory Data Analysis, Distance Measure Model Development, Visualizations, and Model Evaluation with Cluster Validity Indices.

Section 1.1 — Project Summary