  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.
