January 18, 2020

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Recurrent neural networks for time series analysis

Recurrent neural networks for time series analysis

Time series 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 Gonalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.

Talk Title Recurrent neural networks for time series analysis
Speakers Bruno Goncalves (Data For Science)
Conference Strata Data Conference
Conf Tag Make Data Work
Location New York, New York
Date September 11-13, 2018
URL Talk Page
Slides Talk Slides
Video

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. Outline: 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.

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