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Presentation: Forecasting with Prophet

Track: Sequential Data: Natural Language, Time Series, and Sound

Location: Cyril Magnin I

Duration: 10:00am - 10:40am

Day of week: Wednesday

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Abstract

Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts – especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical, modular approach to forecasting “at scale” based on a flexible curve fitting procedure that produces high quality forecasts across a wide variety of business time series.

Speaker: Sean Taylor

Core Statistics Team @Facebook

Sean J. Taylor is a computational social scientist on Facebook's Core Data Science team specializing in field experiments, statistical modeling, and causal inference. His research interests include social influence processes and combining human expertise with machine learning. Sean received his Ph.D. in information systems at NYU’s Stern School of Business and holds a B.S. in economics from The University of Pennsylvania.

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2019 Tracks

  • Sequential Data: Natural Language, Time Series, and Sound

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.