On prediction with fractionally differenced ARIMA models. (English) Zbl 0668.62069
This paper considers some extended results associated with the predictors of long-memory time series models. These direct methods of obtaining predictors of fractionally differenced autoregressive integrated moving- average (ARIMA) processes have advantages from the theoretical point of view.
MSC:
| 62M20 | Inference from stochastic processes and prediction |
Keywords:
Hilbert space; optimal predictors; conditional expectation; orthogonal projection; predictors of long-memory time series models; predictors of fractionally differenced autoregressive integrated moving-average (ARIMA) processesReferences:
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