Mar 15–18, 2020

Time series modeling: ML and deep learning approaches (Day 2)

Bruno Goncalves (Data For Science)
Location: 212 D

Level

Intermediate

Outline

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

Prerequisite knowledge

  • Experience with basic Python programming

What you'll learn

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

Bruno Goncalves

Data For Science

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.

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