Financial forecasting is a di cult task due to the intrinsic com plexity of the nancial system A simpli ed approach in forecasting is given by black box methods like neural networks that assume little about the structure of the economy In the present paper we relate our experience using neural nets as nancial time series forecast method In particular we show that a neural net able to forecast t...

review and classification of electric load forecasting (lf) techniques based on artificial neuralnetworks (ann) is presented. a basic anns architectures used in lf reviewed. a wide range of annoriented applications for forecasting are given in the literature. these are classified into five groups:(1) anns in short-term lf, (2) anns in mid-term lf, (3) anns in long-term lf, (4) hybrid anns inlf,...

study of the changes in the stock price in tehran stock exchange is of great importance. this is because of its application in forecasting the stock price in the stock exchange. the aim of this article is to investigate the forces and mechanisms that cause the dramatic changes in stock price and the formation of chaotic trend. to test whether the chaotic trend in the tehran stock exchange exist...

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York: New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniqu...

This research evaluated forecasting accuracy of hedonic price models based on a number of different submarket assumptions. Using home sale data for the City of Knoxville and vicinities merged with geographic information, we found that forecasting housing prices with submarkets defined using expert knowledge and by school district and combining information conveyed in different modeling strategi...

In a competitive electricity market environment, power producers and consumers need, on one hand, accurate load and/or electricity consumption forecasting tools. These tools will ensure an a-priori knowledge on the amount of energy needed for production. On the other hand, forecasting electricity prices, may play a very important role for producers and consumers when planning bidding strategies...

Price forecasting is an integral part of economic decision making. Forecasts may be used in numerous ways; specifically, individuals may use forecasts to try to earn income from speculative activities, to determine optimal government policies or to make business decisions. The importance of this topic is caused by instability in the world economy and stock markets; there is a growing interest i...

We consider a supply chain consisting of one supplier selling through one retailer who faces a newsvendor problem. There is a positive probability that the retailer is capable of gaining improved demand information through costly forecasting. The supplier would like to induce the retailer to forecast and share that information. Restricting the retailer’s ability to return unsold product would i...

This paper presents an integration prediction method which is called a hybrid forecasting system based on multiple scales. In this method, the original data are decomposed into multiple layers by the wavelet transform and the multiple layers are divided into low-frequency, intermediate-frequency and high-frequency signal layers. Then autoregressive moving average models, Kalman filters and Back...

Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble forecasting model integrating empirical mode decomposition (EMD), genetic algorithm (GA) and artificial neural network (ANN) is proposed to foreca...