FORECASTING ESTIMATED MISSING TIME SERIES DATA

in this article, we make use of the previous techniques in order to forecast two groups of financial time series data with missing data and without missing data. Real closed price data were collected from Amman Stock Exchange (ASE) in order to improve the forecasting accuracy and in solving missing data problem. The result of the study was ensured with the naive test and Theil?s statistic which was found that U = 0.535 for the original data and U = 6213.0 for the suggested data with missing data, respectively. Therefore, the suggested model is significant