Mar 15–18, 2020

Time series modeling: ML and deep learning approaches

Bruno Goncalves (Data For Science)
Sunday, March 15—Monday, March 16
Location: 212 D

Participants should plan to attend both days of training course. Note: to attend training courses, you must be registered for a Platinum or Training pass; does not include access to tutorials on Monday.

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. Bruno Goncalves explains a broad range of traditional machine learning (ML) and deep learning techniques to model and analyze time series datasets with an emphasis on practical applications.

What you'll learn, and how you can apply it

  • Understand the intuition and fundamental techniques underlying the practical analysis of real-world time series datasets




  • Experience with basic Python programming

Hardware and/or installation requirements:

  • Laptop with a scientific Python distribution installed


Understanding time series

  • Empirical examples
  • Trends
  • Seasons and cycles

Programming review

  • pandas
  • scikit-learn
  • statsmodels
  • Keras

Analyzing time series data

  • Timesseries transformations (diff, lag, sqrt, etc.)
  • Resampling and fill methods
  • Bootstrapping and Jacknife
  • Autocorrelations and partial autocorrelation function
  • Correlations of two time series
  • Visualizing time series

Random walks

  • White noise
  • Drift
  • Smoothing and rolling window
  • Fast Fourier Transform

ARIMA models

  • Auto regressive (AR) models
  • Moving averages (MA)
  • Fitting ARIMA models
  • Seasonal ARIMA models

Machine learning with time series

  • Interpolation
  • Time varying features
  • Classification and regression
  • Cross-validation
  • Caveats when working with time series

Deep learning approaches

  • Feed forward networks
  • Recurrent neural networks
  • Gated recurrent units
  • Long short-term memory

About your instructor

Bruno Gonçalves is a chief data scientist at Data For Science, working at the intersection of data science and finance. Previously, he 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 in 2008, he’s 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. In 2015, he was awarded the Complex Systems Society’s 2015 Junior Scientific Award for “outstanding contributions in complex systems science” and in 2018 was named a science fellow of the Institute for Scientific Interchange in Turin, Italy.

Twitter for bgoncalves

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