Deep learning for time series analysis has made rapid progress in 2017 and 2018, with advances in the use of both convolutional and recurrent neural network architectures. At long last, novel architectural motifs and corresponding best practices have been developed specifically for time series data. This has led to significant progress for classifying time series and forecasting the future in many domains, such as speech recognition and demand forecasting.
In this talk, Aileen Nielsen shares the best of these novel architectures to bring you up to speed on the state of the art for a variety of industry time series use cases. Until recently, time series analysis has lagged far behind other areas in the use of deep learning to augment traditional machine learning, but this is changing and it’s a good time to catch up on emerging research. Aileen will also sketch out developing best methods for industry practitioners looking to make forecasts and will conclude with an overview of how industry and academia are developing more standardized metrics and data sets for time series analysis, akin to what has been done in other use cases, such as image classification.
Aileen Nielsen works at an early-stage NYC startup that has something to do with time series data and neural networks, and she’s the author of a Practical Time Series Analysis (2019) and an upcoming book, Practical Fairness, (summer 2020). Previously, Aileen worked at corporate law firms, physics research labs, a variety of NYC tech startups, the mobile health platform One Drop, and on Hillary Clinton’s presidential campaign. Aileen is the chair of the NYC Bar’s Science and Law Committee and a fellow in law and tech at ETH Zurich. Aileen is a frequent speaker at machine learning conferences on both technical and legal subjects.
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