In this work we introduce a forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this ∗Accepted for publication in International Journal of Forecasting. Expected to appear in 2011.

Load forecasting in power systems is an important subject and has been studied from different points of view in order to achieve better load forecasting results. ”Ius paper will address one of the challenges in spatial load forecasting area urban re-development, and present a theory and methodology to incorporate urban re-development into spatial load forecasting considerations.

The forecasting of intermittent demand is a difficult task because of the irregular behavior of the demand process. As a result, the selection of an effective forecasting technique can be challenging. We present an object-oriented software framework for intermittent demand forecasting and inventory analysis. The object-oriented structure of this framework allows easy implementation and integrat...

We examine the relation between the importance for firms to meet external performance benchmarks and the role of internal forecasting and misreporting for increasing the likelihood of meeting benchmarks. Drawing on survey data from investment centers, we hypothesize and find that the importance of meeting benchmarks is positively associated with the sophistication of firms’ internal forecasting...

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (I...

artificial neural networks (anns) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. however, despite of all advantages cited for artificial neural networks, they have data limitation and need to the large amount of historical data in order to yield accurate results. therefore, the...

The notion of representative statistical ensembles, correctly representing statistical systems, is strictly formulated. This notion allows for a proper description of statistical systems, avoiding inconsistencies in theory. As an illustration, a Bose-condensed system is considered. It is shown that a self-consistent treatment of the latter, using a representative ensemble, always yields a conse...

In weather and climate prediction studies it often turns out to be the case that the multi-model ensemble mean prediction has the best prediction skill scores. One possible explanation is that the major part of the model error is random and is averaged out in the ensemble mean. In the standard multi-model ensemble approach, the models are integrated in time independently and the predicted state...

‎This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...