Applying Regression Conformal Prediction with Nearest Neighbors to time series data
This work addresses the problem of generating valid predictive intervals for time series forecasting, which is incremental as it adapts existing conformal prediction methods to a non-exchangeable context.
The paper tackled the challenge of applying conformal prediction to time series data, where exchangeability assumptions fail due to dependencies, by using a nearest neighbors method with fast parameter tuning, and demonstrated its effectiveness in constructing reliable prediction intervals.
In this paper, we apply conformal prediction to time series data. Conformal prediction isa method that produces predictive regions given a confidence level. The regions outputs arealways valid under the exchangeability assumption. However, this assumption does not holdfor the time series data because there is a link among past, current, and future observations.Consequently, the challenge of applying conformal predictors to the problem of time seriesdata lies in the fact that observations of a time series are dependent and therefore do notmeet the exchangeability assumption. This paper aims to present a way of constructingreliable prediction intervals by using conformal predictors in the context of time series. Weuse the nearest neighbors method based on the fast parameters tuning technique in theweighted nearest neighbors (FPTO-WNN) approach as the underlying algorithm. Dataanalysis demonstrates the effectiveness of the proposed approach.