Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Recurrent Neural Networks for timeseries analysis

Bruno Gonçalves (New York University)
1:30pm–5:00pm Tuesday, 09/11/2018
Data science and machine learning
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Temporal data and time-series analytics

Who is this presentation for?

Data Scientists, Data Analysits and anyone with an interest in RNN and timeseries

Prerequisite knowledge

- Python programming - Familiarity with Keras

Materials or downloads needed in advance

python 3 with keras and tensorflow installed

What you'll learn

Timeseries analysis, Recurrent Neural Networks, Gated Recursive Unit, Long Short Term Memory


From the closing price of the stock market to the number of clicks per second on a webpage 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. Their study and understanding requires us to take in to account the sequence of values seen in previous steps and even long term temporal correlations.

In this tutorial we will explore how to use Recurrent Neural Networks, a technique originally developed for Natural Language processing, to model and forecast time series. Their advantages and disadvantages with respect to more traditional approaches will be highlighted.

  1. Recurrent Neural Networks
    – Review of Feed-forward networks
    – Introducing recursion
    – Types of Recurrent Neural Networks
    – Our first recurrent network
  1. Gated Recurrent Units
    – Advantages of recursion
    – Controlling information flow
    – Gates and internal logic
  1. Long-Short Term Memory
    – Remembering the past
    – Avoiding vanishing gradients
    – Memory cells

All code and slides presented during the tutorial will be made available in GitHub:

Photo of Bruno Gonçalves

Bruno Gonçalves

New York University

Bruno Gonçalves is a Moore-Sloan fellow at NYU’s Center for Data Science. With a background in physics and computer science, Bruno has spent his career exploring the use of datasets from sources as diverse as Apache web logs, Wikipedia edits, Twitter posts, epidemiological reports, and census data to analyze and model human behavior and mobility. More recently, he has been focusing on the application of machine learning and neural network techniques to analyze large geolocated datasets.

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