Many of the existing autoregressive moving average (ARMA) forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS) for two factors by calculating ...

According to International Energy Outlook 2007 the total world demand of energy is projected to increase through 2030 about 95% for the non-OECD region and 24% for OECD nations. Crude oil is one of the most critical energy commodities while with coal and natural gas are projected to provide roughly the 86% share of the total US primary energy supply in 2030. In this paper, we use wavelet neural...

This paper develops and tests procedures for identifying lead analysts based on the timeliness of analyst forecast revisions, the trading levels associated with these revisions, and forecast accuracy. Our framework provides an objective assessment of analyst quality that differs from the standard approach that uses survey evidence to rate analysts. Using a sample of equity analysts, we find tha...

In this paper we propose and implement a fusion model by combining the Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to forecast financial market behaviour. The developed tool can be used for in depth analysis of the stock market. Using ANN, the daily stock prices are transformed to independent sets of values that become input to HMM. We draw on GA to o...

This paper proposes a novel non-parametric approach for the analysis and prediction of electricity price curves by applying the manifold learning methodology. Cluster analysis based on the embedded low-dimensional manifold of the original price data is employed to identify characteristics of the price curve shape. The proposed price curve model performs well in forecasting both short-term price...

Abstract- In a typical competitive electricity market, a large number of short-term and long-term contracts are set on basis of energy price by an Independent System Operator (ISO). Under such circumstances, accurate electricity price forecasting can play a significant role in improving the more reasonable bidding strategies adopted by the electricity market participants. So, they cannot only r...

In this paper, next-day hourly forecasts are calculated for the energy price in the electricity production market of Spain. The methodology used to achieve these forecasts is based on artificial neural networks, which have been used successfully in recent years in many forecasting applications. The days to be forecast include working days as well as weekends and holidays, due to the fact that e...

the purpose of this study is to examine the dynamic effects of some macroeconomic variables: money stock, gross domestic product, consumer price index and exchange rates on determining housing price index behavior in iran using the error correction model. using seasonal data, the model is estimated by johansen-juselius cointegration approach during 1990-2007. the results reveal that all variabl...