In order to predict and forecast with greater accuracy, handling “missing values” in “time series” information is crucial. Complete accurate historical data are essential. There many research studies on multivariate time series imputation, however due the lack of associated factors, imputation univariate rarely taken into consideration. It natural that could arise because almost all scientific ...

Two nonparametric methods are presented for forecasting functional time series (FTS). The FTS we observe is a curve at discrete-time point. We address both one-step-ahead and dynamic updating. Dynamic updating forward prediction of the unobserved segment most recent curve. Among two proposed methods, first one straightforward adaptation to k -nearest neighbors univariate forecasting. second bas...