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.
