The demand forecasting technique which is modeled by artificial intelligence approaches using artificial neural networks. The consumer product causers the difficulty in forecasting the future demand and the accuracy of the forecast In performance of the artificial neural network an advantage in a constantly changing business environment and demand forecasting an organization in order to make ri...

This paper evaluates the usefulness of neural networks in GDP forecasting. It is focused on comparing a neural network model trained with genetic algorithm (GANN) to a backpropagation neural network model, both used to forecast the GDP of Albania. Its forecasting is of particular importance in decision-making issues in the field of economy. The conclusion is that the GANN model achieves higher ...

This paper develops a flexible approach to combine forecasts of future spot rates with forecasts from time-series models or macroeconomic variables. We find empirical evidence that accounting for both regimes in interest rate dynamics and combining forecasts from different models helps improve the out-of-sample forecasting performance for US short-term rates. Imposing restrictions from the expe...

Multiple Instance Learning is concerned with learning from sets (bags) of feature vectors (instances), where the bags are labeled, but the instances are not. One of the ways to classify bags is using a (dis)similarity space, where each bag is represented by its dissimilarities to certain prototypes, such as bags or instances from the training set. The instance-based representation preserves the...

river flow forecasting for a region has a special and important role for optimal allocation of water resources. in this research, for forecasting river flow process, fuzzy inference system (fis) is used. three parameters including precipitation, temperature and daily discharge are used for forecasting of daily river flow of lighvan river located in lighvanchai watershed. for the initial preproc...

In this paper, we bridge the gap between hyperparameter optimization and ensemble learning by performing Bayesian optimization of an ensemble with regards to its hyperparameters. Our method consists in building a fixed-size ensemble, optimizing the configuration of one classifier of the ensemble at each iteration of the hyperparameter optimization algorithm, taking into consideration the intera...