Conformal Prediction forecasting with Nixtla’s statsforecast
Conformal Prediction is a framework designed to quantify uncertainty, and it’s quickly becoming a favoured approach in both the corporate world and academic research. Its ease of application sets it apart; with minimal adjustments, it can be integrated into any predictive model.
This allows for the generation of prediction intervals that are accurately calibrated. Unlike any other uncertainty quantification methods, Conformal Prediction produces intervals with a precise level of coverage defined by the user. For instance, a 95% prediction interval generated using this method will encapsulate 95% or more data points in out-of-sample prediction intervals.
The quest for reliable and interpretable prediction intervals has become increasingly pivotal in predictive modelling. Enter Conformal Prediction, a paradigm that has garnered significant attention for its promise in this realm. Born from Kolmogorov’s complexity, Conformal Prediction is a machine-learning framework that furnishes predictions with a measure of their trustworthiness.
At its core, Conformal Prediction is about associating each prediction with a confidence level, ensuring that the error of the predictions falls outside the confidence interval only a fraction of the time that corresponds to (1-confidence level). In other words, if we predict with 95% confidence, we can be 95% sure that the actual value will lie within that prediction interval.
This is especially crucial in fields like finance, healthcare, and energy, where the consequences of erroneous predictions can be profound.
The significance of Conformal Prediction in forecasting is manifold:
**Reliability**: Traditional prediction methods often provide point estimates, leaving users needing clarification about the reliability of these estimates. Conversely, Conformal Prediction offers a systematic way to provide prediction intervals, illuminating the range in which the actual value is likely to fall.
**Adaptability**: Conformal Prediction is distribution-free, unlike methods requiring strong assumptions about the data. This makes it versatile and adaptable to a wide range of applications and datasets.