The goal of the proposed project is to identify economic and regulatory factors that have generated price movements in the pulp & paper industry in order to meet a tactical industry objective of developing improved models for understanding pricing behavior and a strategic industry objective of ensuring the long term economic health of the industry. The proposed project will employ advanced econ...

Currently, stock price forecasting is carried out using either time series prediction methods or trend classifiers. The trend classifiers are designed to predict the behaviour of stock price’s movement. Recently, soft computing methods, like support vector machines, have shown promising results in the realization of this particular problem. In this paper, we apply several prototype generation c...

Financial forecasting is a difficult task due to the intrinsic complexity of the financial system. A simplified 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 financial time series forecast method. In particular we show that a neural net able to...

The use of decision rules and estimation techniques is increasingly common for decision making. In recent years studies were conducted which applies Genetic Programming (GP) to obtain rules to make predictions. A new branch in the area of Evolutionary Algorithms (EA) is Linear Genetic Programming (LGP). LGP evolves instructions sequences of an imperative programming language. This paper propose...

Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...

Internet-based virtual futures markets (VFMs) have been used in predicting election results and movie ticket sales. We construct an Internet-based VFM to predict an underlying stock price. Results of Granger causality tests and tests of directional accuracy show that a VFM with only a small number of participants (75) is able to generate informative futures prices useful in the prediction of th...

A combined wavelet transform (WT) and multiple linear regression (MLR) based technique to forecast price profile in a single settlement real time electricity market has been presented. The historical price and load data has been decomposed into better-behaved wavelet domain constitutive subseries using WT and then combined with other time domain variables to form the set of input variables for ...

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

This paper aims to propose a new framework for estimating and forecasting diffusion of high technology products, along with the construction of a price index. Into that context, the “diffusion–price” model is presented, as an innovative concept providing a long term estimation of both price and diffusion elasticity. This corresponds to the bidirectional estimation of the mutual influence of the...