Financial time series has been standard complex problem in the field of forecasting due to its non-linearity and high volatility. Though various neural networks such as backpropagation, radial basis, recurrent and evolutionary etc. can be used for time series forecasting, each of them suffer from some flaws. Performances are more varied for different time series with loss of generalization. Eac...

Wavelet analysis, as a promising technique, has been used to approach numerous problems in science and engineering. Recent years have witnessed its novel application in economic and finance. This paper is to investigate whether features (or indicators) extracted using the wavelet analysis technique could improve financial forecasting by means of Financial Genetic Programming (FGP), a genetic pr...

Financial market prediction is a critically important research topic in financial data mining because of its potential commerce application and attractive profits. Previous studies in financial market prediction mainly focus on financial and economic indicators. Web information, as an information repository, has been used in customer relationship management and recommendation, but it is rarely ...

The application of fuzzy approach models to forecast has been drawing a lot of attention. This study proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based upon a single objective may not make the best use of available information for a variety of reasons. Combined forecast...

Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory. Using the basic structure of the fMSV model, the authors extend the dynamic correlation MSV model, the co...

the sales proceeds are the most important factors for keeping alive profitable companies. so sales and budget sales are considered as important parameters influencing all other decision variables in an organization. therefore, poor forecasting can lead to great loses in organization caused by inaccurate and non-comprehensive production and human resource planning. in this research a coherent so...

time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. forecasting accuracy is one of the most important features of forecasting models. nowadays, despite the numerous time series forecasting models which have been proposed in several past decades, it is widely recognized that financial markets are extremely difficult to ...

The difficulty in gas price forecasting has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting gas prices however, all of the existing models of prediction cannot meet practical needs. In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration of GMDH neural n...

Intelligent time-series forecasting is important in several applied domains. Artificially intelligent methods for forecasting are being consistently sought. The effect of noise on time-series prediction is important to quantify for accurate forecasting with these systems. Conventionally, noise is considered obstructive to accurate forecasting. In this paper we analyse the noise impact on time-s...