this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

Forecasting financial markets is an important issue in finance area and research studies. On one hand, the importance of prediction, and on the other hand, its complexity, have led to huge number of researches which have proposed many forecasting methods in this area. In this study, we propose a hybrid model including Wavelet Transform, ARMA-GARCH and Artificial Neural Network (ANN) for single-...

background and objectives: efficient cost management in hospitals’ pharmaceutical inventories have thepotential to remarkably contribute to optimization of overall hospital expenditures. to this end, reliable forecasting models for accurate prediction of future pharmaceutical demands are instrumental. while the linear methods are frequently used for forecasting purposes chiefly due to their sim...

Background and Objectives: Efficient cost management in hospitals’ pharmaceutical inventories have the potential to remarkably contribute to optimization of overall hospital expenditures. To this end, reliable forecasting models for accurate prediction of future pharmaceutical demands are instrumental. While the linear methods are frequently used for forecasting purposes chiefly due to their si...

We propose a simple way of predicting time series with reoccurring seasonal periods. We combine several forecasting methods by taking the samplewise weighted mean of those forecasts that were generated with models showing low prediction errors on left-out parts of the time-series. We show the application of this approach to the NN5 Time Series Competition data set.

This paper describes a new type of ensembles that aims at improving the predictive performance of these approaches in time series forecasting. Ensembles are recognised as one of the most successful approaches to prediction tasks. Previous theoretical studies of ensembles have shown that one of the key reasons for this performance is diversity among ensemble members. Several methods exist to gen...

Businesses are recognizing the value of data as a strategic asset. This is reflected by the high degree of interest in new technologies such as data mining. Corporations in banking, insurance, retail, and healthcare are harnessing aggregated operational data to help understand and run their businesses (Brockett et al., 1997; Delmater & Hamcock, 2001). Analysts use data-mining techniques to extr...