Demystifying EnbPI: Mastering Conformal Prediction Forecasting

“In my article ‘Conformal Prediction Forecasting with MAPIE,’ we explored how Conformal Prediction enables us to construct powerful probabilistic forecasting models. Unlike other probabilistic forecasting methods, Conformal Prediction forecasting models can generate prediction intervals encapsulating ground truth values at user-determined coverage levels. In this article, we will dive deep into the mechanics of EnbPI under the hood.

make every forecast count

EnbPI is a Conformal Prediction method that can construct distribution-free prediction intervals for time series; the technique wraps around any bootstrap ensemble estimator. Unlike classical Conformal Prediction methods, it does not require data exchangeability assumption.

In simpler terms, the data exchangeability assumption implies that the sequence in which the observations appear in the dataset is irrelevant. Changing the order of the observations won’t alter the overall statistical properties of the data.

However, in time series analysis, this exchangeability assumption does not hold, as the order of the data points often carries significant information. This necessitates the development of a particular class of Conformal Prediction methods designed to handle time series data, where the order is crucial.

Conformal Prediction for time series has become a popular 🔥🔥🔥🔥🔥 and rapidly growing 🚀🚀🚀🚀🚀 area in both research and practical applications, leading to the creation of many robust models in academia and industry.

EnbPI does not require data exchangeability and wraps around any bootstrap ensemble estimator to construct sequential prediction intervals. EnbPI is generally easy to implement, computationally efficient, and well-suited to various regression functions.

EnbPI was the first Conformal Prediction model developed explicitly for time series by researchers from Georgia Tech, who presented the paper at ICML 2021 Conformal Prediction track. Since then, the model has been implemented in multiple Conformal Prediction libraries, including MAPIE and Amazon Fortuna.

EnbPI is suitable for non-stationary time series; prediction intervals produced by EnbPI enjoy approximately valid marginal coverage under mild assumptions on…

Valeriy Manokhin, PhD, MBA, CQF

Principal Data Scientist, PhD in Machine Learning, creator of Awesome Conformal Prediction 👍Tip: hold down the Clap icon for up x50

Recommended from Medium


See more recommendations