Estimation and forecasting of long-memory processes with missing values

A method incorporating state-space models with the Kalman filter can be used to assess the possibility of an efficient estimation and forecasting of a long-memory time series with missing values. The method permits a productive estimation of a fractionally integrated autoregressive moving average (ARIFMA) process and future values with missing data. Its performance was proven effective using a foreign exchange data set.