Time series forecasting and anomaly detection is of utmost importance at Uber. However, the scale of the problem, the need for speed, and the importance of accuracy make anomaly detection a challenging data science problem. Andrea Pasqua and Anny Chen explain how the use of recurrent neural networks is allowing Uber to meet this challenge.
Andrea Pasqua is a data science manager at Uber, where he leads the time series forecasting and anomaly detection teams. Previously, Andrea was director of data science at Radius Intelligence, a company spearheading the use of machine learning in the marketing space; a financial analyst at MSCI, a leading company in the field of risk analysis; and a postdoctoral fellow in biophysics at UC Berkeley. He holds a PhD in physics from UC Berkeley.
Anny (Yunzhu) Chen is a senior data scientist at Uber working on time series anomaly detection and forecasting. Anny is passionate about applying statistical and machine learning models to real business problems. Previously, she was a data scientist at Adobe, where she worked on digital attribution modeling for customer conversion data. She holds an MS in statistics from Stanford University and a BS in probability and statistics from Peking University.
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