Owing to the sporadic nature of demand for aircraft maintenance repair parts, airline operators perceive dif%culties in forecasting and are still looking for superior forecasting methods. This paper deals with techniques applicable to predicting spare parts demand for airline 4eets. The experimental results of 13 forecasting methods, including those used by aviation companies, are examined and ...

This study presents the implication of Bayesian technique on simple statistical and econometric forecasting models to improve the forecast performances of the models. We consider a nonlinear, non-stationary time series of household electricity demand to demonstrate the Bayesian implication to statistical techniques. In this forecasting process, the electricity demand is considered a function of...

Automatic forecasts of large numbers of univariate time series are often needed in business. It is common to have over one thousand product lines that need forecasting at least monthly. In these circumstances, an automatic forecasting algorithm is an essential tool. Automatic forecasting algorithms must determine an appropriate time series model, estimate the parameters and compute the forecast...

This study evaluates the performance of three alternative models for forecasting daily interbank exchange rate of U.S. dollar measured in Pak rupees. The simple ARIMA models and complex models such as GARCH-type models and a state space model are discussed and compared. Four different measures are used to evaluate the forecasting accuracy. The main result is the state space model provides the b...

An increased number of intermittent renewables poses a threat to the system balance. As a result, new tools and concepts, like advanced demand-side management and smart grid technologies, are required for the demand to meet supply. There is a need for higher consumer awareness and automatic response to a shortage or surplus of electricity. The distributed water heater can be considered as one o...

The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artifi cial neural networks are applied to forecast the remaining nonlinearities in the shocks of the SARIMA model. In this paper, we propose a simple nonlinear time series forecasting model by ...

Introduction. In order to develop a dependable, quantitative means to anticipate and forecast requirements for the New Jersey nursing workforce, we convened a Work Group to determine the data needed and the data available. A labor economist provided consultation to the Work Group. Together, we reviewed the most recent version of the Nurse Demand-Based Requirements Forecasting Model developed fo...

I n this paper, we specify that the GARCH(1,1) model has strong forecasting volatility and its usage under the truncated standard normal distribution (TSND) is more suitable than when it is under the normal and student-t distributions. On the contrary, no comparison was tried between the forecasting performance of volatility of the daily return series using the multi-step ahead forec...

Many organizations need to forecast large numbers of time series that are discretely valued. These series, called count series, fall approximately between continuously valued time series, for which there are many forecasting techniques (ARIMA, UCM, ESM, and others), and intermittent time series, for which there are few forecasting techniques (Croston’s method and others). This paper proposes a ...

For univariate forecasting, there are various statistical models and computational algorithms available. In real-world exercises, too many choices can create difficulties in selecting the most appropriate technique, especially for users lacking sufficient knowledge of forecasting. This paper provides evidence, in the form of an empirical study on forecasting accuracy, to show that there is no b...