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.

翻译：在本文中,我们对时间序列数据进行一致预测。 非正式预测是一种产生具有信心水平的预测区域的方法。 在可交换性假设中,区域产出总是有效的。 但是,这一假设并不能维持时间序列数据,因为过去、现在和未来观测之间有联系。 因此,对时间序列数据问题应用一致预测数据的挑战在于对时间序列的观察取决于时间序列的观察,因此不考虑可交换性假设。 本文的目的是通过在时间序列中使用符合预测值的预测值来提出一种构建可靠的预测间隔的方法。 我们使用基于加权近邻(FPTO-WNN)方法快速参数调整技术的近邻方法作为基本算法。 数据分析表明拟议方法的有效性。