Neural networks are good at classification, forecasting and recognition. They are also good candidates of financial forecasting tools. Forecasting is often used in the decision making process. Neural network training is an art. Trading based on neural network outputs, or trading strategy is also an art. We will discuss a seven-step neural network forecasting model building approach in this arti...

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 ...

In this paper, we compare the forecasting accuracy of two neural network models in forecasting earnings per share of Chinese listed companies based upon fundamental accounting variables. In one neural network model, weights estimated by back propagation were utilised, and in the other model a genetic algorithm was utilised. Based upon a sample of 723 Chinese companies in 22 industries over a te...

This paper investigates artificial neural networks prediction modeling of foreign currency rates using Levenberg Marquardt (LM) learning algorithms. The models were trained from historical data using US Dollar (USD) currency rates against Indonesian Rupiah (IDR). The forecasting performance of the models was evaluated using a number of statistical measurements and compared. The results show tha...

This paper proposes the combined forecasting model which study on the classic swine fever (CSF) morbidity, using the forecasting results of ARIMA and GM (1, 1) model as the inputs of the majorizing BP neural network. Analyzing the monthly data from 2000 to 2009 and the accuracy of the forecasting results is 97.379%, more accurate and more steady than traditional methods. This research provides ...

Performance of inventory management depends on the accuracy of demand forecasting. There are many techniques used for forecasting demand in retail sale. Advances in data mining application systems have given rise to the use of business intelligence in various domains of retailing. The current research captures the knowledge of classification of the customers using the purchase-based data of cus...

Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting...

The accurate forecasting of carbon dioxide (CO2) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the di...

As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied in financial time series forecasting. In the modeling of financial time series using SVR, one ...