User Guide

The following guides cover how to get started with a pmdarima distribution. The easiest solution is simply installing from PyPi, but if you’d like to contribute you’ll need to be able to build from source.

• 1. About the project
  • 1.1. The name…
  • 1.2. How it works
    • 1.2.1. How auto_arima works
  • 1.3. Feedback
• 2. Setup
  • 2.1. Install from PyPi
  • 2.2. Build from source
• 3. Quickstart
  • 3.1. Auto-ARIMA example
• 4. Serializing your ARIMA models
• 5. Refreshing your ARIMA models
  • 5.1. Updating your model with new observations
• 6. Tips to using auto_arima
  • 6.1. Understand p, d, and q
    • 6.1.1. Understanding differencing (d)
    • 6.1.2. Enforcing stationarity
  • 6.2. Understand P, D, Q and m
    • 6.2.1. Estimating the seasonal differencing term, D
    • 6.2.2. Setting m
  • 6.3. Parallel vs. stepwise
  • 6.4. Pipelining
• 7. Toy time-series datasets
  • 7.1. Air Passengers
  • 7.2. Austres
  • 7.3. Heartrate
  • 7.4. Lynx
  • 7.5. Wineind
  • 7.6. Woolyrnq
• 8. Contributing to pmdarima
  • 8.1. How to contribute
  • 8.2. Pull Request Checklist
  • 8.3. Filing bugs
• 9. Contributors