Feature Engineering for Time Series Data
Feature Engineering for Time Series Data


Most machine learning algorithms today are not time-aware and are not easily applied to time series and forecasting problems. Leveraging algorithms like XGBoost, or even linear models, typically requires substantial data preparation and feature engineering – for example, creating lagged features, detrending the target, and detecting periodicity. The preprocessing required becomes more difficult in the common case where the problem requires predicting a window of multiple future time points. As a result, most practitioners fall back on classical methods, such as ARIMA or trend analysis, which are time-aware but often less expressive. This talk covers practices for solving this challenge and exploring the potential to automate this process in order to apply advanced machine learning algorithms time series problems.


Michael Schmidt is the Chief Scientists at DataRobot, and has been featured in the Forbes list of the world’s top 7 data scientists and MIT’s list of the most innovative 35-under-35. He has authored AI research in the journal Science and has appeared in media outlets such as the New York Times, NPR’s RadioLab, the Science Channel, and Communications of the ACM. In 2011, Michael founded Nutonian and led the development Eureqa, a machine learning application and service used by over 80,000 users and later acquired by DataRobot in 2017. Most recently, his work has focused on automated machine learning, feature engineering, and time series prediction.

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