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Time Series

Time series forecasting

Published by O'Reilly Media, Inc.

Build and deploy your machine learning models to forecast the future

January 23, 2019

4:00 p.m. - 7:00 p.m. Coordinated Universal Time

This event has ended.

What you’ll learn and how you can apply it

By the end of this live online course, you’ll understand:

  • What makes time series special
  • Loading and handling time series in pandas
  • How to check stationarity of a time series
  • How to make a time series stationary
  • The basics of recurrent neural networks (RNN) and advanced RNN architectures, including LSTM and GRU

And you’ll be able to:

  • Use classical methods for time series forecasting
  • Determine when to use RNNs instead of traditional time series models
  • Employ techniques and tricks for building successful machine learning-based time series forecasting models

This live event is for you because…

  • You're a business analyst or a data scientist who needs to build time series forecasting models.
  • You're a developer who needs to operationalize your time series forecasting models.


  • Experience coding in Python
  • A basic understanding of machine learning and deep learning topics and terminology as well as the mathematics used for machine learning
  • A laptop with an up-to-date version of the Edge or Chrome browser and the Azure Machine Learning Python SDK installed
  • A GitHub account
  • An Azure Notebooks account

Recommended follow-up:


The timeframes are only estimates and may vary according to how the class is progressing.

Introduction to time series forecasting (25 minutes)

  • Lecture: What makes time series special?; loading and handling time series in pandas; how to check stationarity of a time series; how to make a time series stationary
  • Q&A

Classical methods for time series forecasting (25 minutes)

  • Lecture: Exponential smoothing (ETS); autoregression (AR); moving average (MA); autoregressive moving average (ARMA); autoregressive integrated moving average (ARIMA)
  • Q&A
  • Break (10 minutes)

Introduction to recurrent neural networks (RNN) for time series forecasting (55 minutes)

  • Lecture: Basic concepts (neurons, layers, weights, bias, and activation functions); cost function; training using stochastic gradient descent and minibatches; backpropagation; early stopping; introduction to recurrent neural networks (RNNs); backpropagation through time (BPTT); vanishing gradient and exploding gradient; comparison of different RNN units—GRU, LSTM
  • Q&A
  • Break (10 minutes)

Build and deploy your own time series forecasting model (55 minutes)

  • Walkthroughs and demonstrations: Simple time series forecasting models with an energy demand forecasting use case; RNN forecasting models with web traffic forecasting and grocery sales forecasting
  • Hands-on exercises: In groups, apply these algorithms to real-world scenarios, using machine learning components available in open source Python packages, such as scikit-learn, Keras, and TensorFlow
  • Group discussion: Comparison of results and performance
  • Wrap-up and Q&A

Bonus exercise: Using your own time series dataset or an available public dataset, such as Rossmann Store Sales or Recruit Restaurant Visitors, build and deploy your own time series model using the Azure Machine Learning service

Your Instructor

  • Francesca Lazzeri

    Francesca Lazzeri, Ph.D. is Senior Machine Learning Scientist at Microsoft on the Cloud Advocacy team and expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries – energy, oil and gas, retail, aerospace, healthcare, and professional services.

    Before joining Microsoft, she was Research Fellow in Business Economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit. At Harvard, she worked on multiple patent, publication and social network data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation.

    Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world. She is Data Science mentor for Ph.D. and Postdoc students at the Massachusetts Institute of Technology, and speaker at academic and industry conferences - where she shares her knowledge and passion for AI, machine learning, and coding.

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