Creating prediction intervals using conformal prediction
In this recipe, we’ll explore how to create prediction intervals. Prediction intervals describe the range of values within which future observations will likely fall with some confidence level. The greater the confidence required, the larger the intervals will be.
In practice, the model predicts not just a single point but a distribution for future observations. Various techniques exist to construct these intervals, including parametric methods that assume a specific distribution of errors and non-parametric methods that use empirical data to estimate intervals.
We’ll resort to a conformal prediction approach, which is increasingly popular among data science practitioners.
Getting ready
We’ll build prediction intervals for an ARIMA model, which is a popular forecasting approach. Yet, conformal prediction is agnostic to the underlying method and can be applied to other forecasting methods.
Let...
