Forecasting Diabetes Patients Attendance at Al-Baha Hospitals Using Autoregressive Fractional Integrated Moving Average (ARFIMA) Models

An Evaluation of ARFIMA (Autoregressive Fractional Integral Moving Average) Programs

Strong coupling between values at different times that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The autoregressive fractional integral moving average (ARFIMA) model, a fractional order signal processing technique, is the generalization of the conventional integer order models—autoregressive integral ...

Forecasting Inflation: Autoregressive Integrated Moving Average Model

This study compares the forecasting performance of various Autoregressive integrated moving average (ARIMA) models by using time series data. Primarily, The Box-Jenkins approach is considered here for forecasting. For empirical analysis, we used CPI as a proxy for inflation and employed quarterly data from 1970 to 2006 for Pakistan. The study classified two important models for forecasting out ...

Forecasting with prediction intervals for periodic autoregressive moving average models

Periodic autoregressive moving average (PARMA) models are indicated for time series whose mean, variance and covariance function vary with the season. In this study, we develop and implement forecasting procedures for PARMA models. Forecasts are developed using the innovations algorithm, along with an idea of Ansley. A formula for the asymptotic error variance is provided, so that Gaussian pred...

Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation

Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK in ation

We discuss computational aspects of likelihood-based speci cation, estimation, inference, and forecasting of possibly nonstationary series with long memory. We use the arfima(p; d; q) model with deterministic regressors and we compare sampling characteristics of approximate and exact rst-order asymptotic methods. We extend the analysis using a higher-order asymptotic method, suggested by Cox an...