This article presents a novel method for prediction of stationary functional time series, in particular trajectories that share similar pattern but display variable phases. The limitation most the existing methodologies series is they only consider vertical variation (amplitude, scale, or shift). To overcome this limitation, we develop shape-preserving (SP) incorporates both and horizontal vari...

A nonparametric method to predict non-Markovian time series of partially observed dynamics is developed. The prediction problem we consider a supervised learning task finding regression function that takes delay-embedded observable the at future time. When delay-embedding theory applicable, proposed consistent estimator flow map induced by delay-embedding. Furthermore, corresponding Mori–Zwanzi...

Time series prediction has been studied for decades due to its potential in a wide range of applications. As one the most popular technical indicators, moving average summarizes overall changing patterns over past period and is frequently used predict future trend time series. However, traditional indicators are calculated by averaging data with equal or predefined weights, ignore subtle differ...

abstract prediction of input flow into water resources is regarded as one of the most important issues in optimum planning and management in producing electro-water energy and optimum allocation of water into different consumption sources. different parameters affect on input discharge into dams. climate variables including temperature and rainfall have the most effect on input runoff rate to w...