From the closing price of the stock market to the number of clicks per second on a web page or the sequence of venues visited by a tourist exploring a new city, time series and temporal sequences of discrete events are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations.
Join Bruno Gonçalves to learn how to use recurrent neural networks, a technique originally developed for natural language processing, to model and forecast time series. You’ll also discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.
Recurrent neural networks
Gated recurrent units
Long short-term memory
All code and slides presented during the tutorial will be made available in the course GitHub repository.
Bruno Gonçalves is a vice president of data science and finance at JPMorgan Chase. Previously, we was a data science fellow at NYU’s Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Université. Since completing his PhD in the physics of complex systems, he has been pursuing the use of data science and machine learning to study human behavior. Using large datasets from Twitter, Wikipedia, web access logs, and Yahoo! Meme, he studied how we can observe both large-scale and individual human behavior in an obtrusive and widespread manner. The main applications have been to the study of computational linguistics, information diffusion, behavioral change, and epidemic spreading.
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