Time series data has many applications, for example in finance, or analyzing server metrics, or monitoring IoT signals. One of the most common tasks is to predict the future based on historical data. Other applications are monitoring and outlier detection.
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.
In this talk we will review time series analysis with a focus on modern machine learning approaches and practical considerations, with recommendations on what works and what does not in which context. The talk will cover the basics of the mathematical underpinnings, and then discuss how to reframe the problem as a general supervised learning problem. It shows how to use non-linear methods, including deep learning. Using practical applications from the industry, it will go through a number of scenarios of increasing complexity and provide practical advice to get started.
Mikio Braun is principal engineer for search at Zalando, one of the biggest European fashion platforms. Mikio holds a PhD in machine learning and worked in research for a number of years before becoming interested in putting research results to good use in the industry.
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