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In this paper, we apply conformal prediction to time series data. Conformalprediction isa method that produces predictive regions given a confidencelevel. The regions outputs arealways valid under the exchangeabilityassumption. However, this assumption does not holdfor the time series databecause there is a link among past, current, and futureobservations.Consequently, the challenge of applying conformal predictors tothe problem of time seriesdata lies in the fact that observations of a timeseries are dependent and therefore do notmeet the exchangeability assumption.This paper aims to present a way of constructingreliable prediction intervalsby using conformal predictors in the context of time series. Weuse the nearestneighbors method based on the fast parameters tuning technique in theweightednearest neighbors (FPTO-WNN) approach as the underlying algorithm. Dataanalysisdemonstrates the effectiveness of the proposed approach.

Applying Regression Conformal Prediction with Nearest Neighbors to time series data