/tags/dtw/Recent content in DTW on Stuart LacyHugo -- gohugo.ioen-uk© 2023 Stuart LacyTue, 21 Apr 2026 00:00:00 +0000/2026/04/21/clustering-running-data-using-dynamic-time-warping/Tue, 21 Apr 2026 00:00:00 +0000/2026/04/21/clustering-running-data-using-dynamic-time-warping/Motivation I recently needed to implement some time-series clustering at work on a high-dimensional dataset. Having not tackled this specific problem before, I wanted to practice on a smaller, univariate dataset where I already had a strong intuition about the underlying structure. I turned to my exported Strava running data as a testbed to get familiar with clustering using Dynamic Time Warping (DTW). Heartrate data Show the code library(tidyverse) library(duckdb) library(plotly) library(dtw) library(parallel) library(tidytext) library(ggwordcloud) library(patchwork) library(dendextend) library(ggridges) library(broom) library(cluster) I started by pulling data from my existing database, restricting the scope to runs that contain both heart rate and GPS location data.