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Wednesday, October 17 • 4:00pm - 4:20pm
Solving the Data Science Time Series Forecasting Challenge

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Most machine learning algorithms today are not time-aware nor easily applied to time series and forecasting problems. Leveraging advanced algorithms like XGBoost or linear models typically requires substantial data preparation and feature engineering.

This presentation will cover the best practices for solving this challenge by introducing a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.

avatar for Michael Schmidt

Michael Schmidt

Chief Data Scientist, DataRobot
Michael is chief data scientist at DataRobot where he works on algorithms and techniques to automate machine learning processes. His research has appeared in the New York Times, NPR's RadioLab, and Communications of the ACM. Michael also created the Eureqa project, a software program... Read More →

Wednesday October 17, 2018 4:00pm - 4:20pm EDT
Horace Mann