Machine Learning

Understand Time Series Forecast Uncertainty Using Prediction Intervals with Python

Last Updated on August 28, 2019

Time series forecast models can both make predictions and provide a prediction interval for those predictions.

Prediction intervals provide an upper and lower expectation for the real observation. These can be useful for assessing the range of real possible outcomes for a prediction and for better understanding the skill of the model

In this tutorial, you will discover how to calculate and interpret prediction intervals for time series forecasts with Python.

Specifically, you will learn:

  • How to make a forecast with an ARIMA model and gather forecast diagnostic information.
  • How to interpret a prediction interval for a forecast and configure different intervals.
  • How to plot the prediction interval in the context of recent observations.

Kick-start your project with my new book Time Series Forecasting With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s dive in.

  • Updated Apr/2019: Updated the link to dataset.
  • Updated Jun/2019: Changed from prediction intervals to prediction intervals.
  • Updated Aug/2019: Updated data loading to use new API.
Understand Time Series Forecast Uncertainty Using Confidence Intervals with PythonTo finish reading, please visit source site