PERBAIKAN HASIL AUTOREGRESSIVE ORDE SATU (AR(1) DENGAN TEKNIK ENSEMBLE KALMAN FILTER - Hery Andi Sitompul (Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Darma Agung (LPPM_UDA), 2018)
The accuracy of the results of a forecasting method is the main consideration for using the forecasting model in statistical decision making. The size of the accuracy can be seen from the percentage of errors generated by the forecasting model. There are various forecasting methods in statistics, where one of them is the autoregressive first order or AR (1) model, this model is known to be good enough to predict a random process value in a short period of time, but by applying the Ensemble Kalman Filter method to forecasting methods this is expected to produce more accurate results. In this paper it will be shown that the Ensemble Kalman Filter (EnKF) technique can improve the forecasting results.