Warping Time: Using Dynamic Time Warping to Improve Machine Learning Outcomes


A lot of people have heard of or calculated euclidean distances but have you used dynamic time warping? It allows us to calculate the distances between arrays, specifically time series in this talk, with different numbers of observations. This talk will focus on introducing dynamic time warping using practical examples. Examples include comparisons of stock performances, audio meta-data, and heartbeats.

How does it work? How are you able to align non-matching time-series? What are we trying to measure? You’ll walk away from this talk with a firm understanding of what dynamic time warping is and how it can be used.

We’ll examine two similar stocks from different stock exchanges to calculate the similarities between their performance, differentiate between two different people using audio meta-data, and compare the similarities between anomalous and normal heartbeats. We’ll review the different packages available (python and R) and talk about the pros and cons of each.

Lastly we’ll talk about how to use dynamic time warping in a machine learning environment. We’ll review some different uses such as features in a classification algorithm or clustering. We’ll take a look at a couple of examples that have been used.

You’ll walk away with a new tool in your time-series analysis tool box with a solid understanding of what dynamic time warping is, how to calculate similarities between two time series, and improve predictions with machine learning algorithms. All examples will be available in Jupyter notebooks in both R and python so you have examples you can take home with you.


Kim Kraunz is the Lead Data Scientist at Mimoto. Mimoto analyzes human, environmental, and contextual factors to recognize active system-level cyberattacks and malicious behavior in real-time. She leads the Data Scientist team in research, prioritization, and implementation of machine learning models for user identification. She has a Doctor of Science degree in Genetics and Complex Diseases from Harvard University and a Bachelor of Science degree in Biology and Environmental Engineering from Washington University. In her spare time, she enjoys hiking and being outdoors in the Truckee area.

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