Applying Regression Conformal Prediction with Nearest Neighbors to time series data

• URL: http://arxiv.org/abs/2110.13031v1
• Date: Mon, 25 Oct 2021 15:11:32 GMT
• Title: Applying Regression Conformal Prediction with Nearest Neighbors to time series data
• Authors: Samya Tajmouati, Bouazza El Wahbi and Mohammed Dakkoun
• Abstract summary: This paper presents a way of constructingreliable prediction intervals by using conformal predictors in the context of time series data. We use the nearest neighbors method based on the fast parameters tuning technique in the nearest neighbors (FPTO-WNN) approach as the underlying algorithm.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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