Wrapping-up

Machine Learning for Time Series Data in Python

Chris Holdgraf

Fellow, Berkeley Institute for Data Science

Timeseries and machine learning

  • The many applications of time series + machine learning
  • Always visualize your data first
  • The scikit-learn API standardizes this process
Machine Learning for Time Series Data in Python

Feature extraction and classification

  • Summary statistics for time series classification
  • Combining multiple features into a single input matrix
  • Feature extraction for time series data
Machine Learning for Time Series Data in Python

Model fitting and improving data quality

  • Time series features for regression
  • Generating predictions over time
  • Cleaning and improving time series data
Machine Learning for Time Series Data in Python

Validating and assessing our model performance

  • Cross-validation with time series data (don't shuffle the data!)
  • Time series stationarity
  • Assessing model coefficient and score stability
Machine Learning for Time Series Data in Python

Advanced concepts in time series

  • Advanced window functions
  • Signal processing and filtering details
  • Spectral analysis
Machine Learning for Time Series Data in Python

Advanced machine learning

  • Advanced time series feature extraction (e.g., tsfresh)
  • More complex model architectures for regression and classification
  • Production-ready pipelines for time series analysis
Machine Learning for Time Series Data in Python

Ways to practice

  • There are a lot of opportunities to practice your skills with time series data.
  • Kaggle has a number of time series predictions challenges
  • Quantopian is also useful for learning and using predictive models others have built.
Machine Learning for Time Series Data in Python

Let's practice!

Machine Learning for Time Series Data in Python

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