In this study, an improved cascaded neural network is utilized to determine a Marketing Clearing Price (MCP) for energy in the wholesale market Russian. Research on MCP prediction has attracted lot of attention recent years. This study recommends unique approach based customized back propagation algorithm retraining, testing, and testing pricing over many months. research contributed developmen...

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This research applies a market profile to establish an indicator to classify the correlation between the variation in price and value with the stock trends. The indicator and technical index are neural network architecture parameters that assist to extrapolate the market logic and knowledge rules that influence the TAIEX futures market structure via an integral assessment of physical quantities...

We study a model in which individual agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a smart Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual forecasting behaviour; (2) an explanation of individual and aggregate behavior in four different experimental ...

Penetration of smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems. Probability density forecasting methods can effectively quantify the uncertainty of power load forecasting. The paper proposes a short-term power load probability density forecasting method using kernel-based support vector quantile regression (KSVQR) and Copula theory. A...

The objective of this study was to model seasonal behavior of broiler price in Iran that can be used to forecast the monthly broiler prices. In this context, the periodic autoregressive (PAR), the seasonal integrated models, and the Box-Jenkins (SARIMA) models were used as the primary nominates for the forecasting model. It was shown that the PAR (q) model could not be considered as an appropri...

in this paper we investigate the long memory of tehran securities price index and fit arfima model using 970 daily data since 1382/1/6 until 1386/4/17. furthermore, we compare the forecasting performance of arfima and arima models. the results show that the series is a long memory one and therefore it can become stationary by fractional differencing. we obtaine the fractional differencing param...