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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Machine learning for time series: What works and what doesn't

Mikio Braun (Zalando)
11:20am–12:00pm Wednesday, 09/12/2018
Data science and machine learning
Location: 1A 15/16 Level: Intermediate
Secondary topics:  Deep Learning, Retail and e-commerce, Temporal data and time-series analytics
Average rating: ****.
(4.86, 7 ratings)

Who is this presentation for?

  • Data scientists and research engineers

Prerequisite knowledge

  • A basic understanding of statistics and data science (useful but not required)

What you'll learn

  • Explore time series analysis concepts
  • Understand how time series analysis can be reformulated as a machine learning problem
  • Learn how more advanced machine learning methods, including deep learning, can be applied to time series analysis


Time series data has many applications in industry, from analyzing server metrics to monitoring IoT signals and outlier detection. One of the most common tasks is to predict the future based on historical data.

Time series analysis has a strong mathematical background in the theory of stationary processes, but recently, machine learning methods, and in particular deep learning, have become quite popular. The flexibility and expressive power of these methods has allowed data scientists to tackle complex real-world problems.

Mikio Braun offers an overview of time series analysis with a focus on modern machine learning approaches and practical considerations, including recommendations for what works and what doesn’t. Mikio covers the mathematical underpinnings and explains how to reframe time series problems as general supervised learning problems and how to use nonlinear methods, including deep learning.

Using practical applications from the industry, Mikio shares a number of scenarios of increasing complexity and provides practical advice to get started, for example: using stacked recurrent neural networks autoencoders for extreme event forecasting at Uber, using deep learning for autoregressive demand forecasting at Amazon, and using recurrent neural networks for customer analysis at Zalando.

Photo of Mikio Braun

Mikio Braun


Mikio Braun is a principal engineer for search at Zalando, one of Europe’s biggest fashion platforms. He worked in research for a number of years before becoming interested in putting research results to good use in the industry. Mikio holds a PhD in machine learning.

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09/13/2018 5:38am EDT

I was unable to hear clearly because the room was so full. Are the slides available?