Deep learning for time series analysis has made rapid progress in 2018 and 2019, 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.
Aileen Nielsen brings you up to speed on the best of these novel architectures so you can catch up 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 also sketches out developing best methods for industry practitioners looking to make forecasts and concludes 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. Previously, Aileen worked at corporate law firms, physics research labs, a variety of NYC tech startups, and most recently, the mobile health platform One Drop as well as on Hillary Clinton’s presidential campaign. Her interests range from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. She also serves as chair of the New York City Bar Association’s Science and Law Committee, which focuses on how the latest developments in science and computing should be regulated and how such developments should inform existing legal practices. Aileen is a frequent speaker at machine learning conferences on both technical and legal subjects.
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