The error of statistical volatility of intra-daily quoted price changes observed over a time interval,Statistical study of foreign exchange rates, empirical evidence of a price change scaling law, and intraday analysis', Jounal of Banking and standard deviation to correct this bias when the observed time scale is large enough. When the time scale is small, however, the R= ~ S statistic introduc...

Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used...

In the paper the problem of the application of neural predictors for Short-Term Load Forecasting (STLF) for energy transactions planning in utility is presented. Several aspects of this topic are discussed, including identification of different load patterns for holidays and customer profiles, estimation of prediction intervals and optimal size of the order, according to the financial condition...

Gas demand possesses dual property of growing and seasonal fluctuation simultaneously, it makes gas demand variation possess complex nonlinear character. From previous studies know single model for nonlinear problem can’t get good results but accurately gas forecast were essential part of an efficient gas system planning and operation. In recent years, lots of scholar put forward combination mo...

This paper provides detailed information on Team Poland’s approach in the electricity price forecasting track of GEFCom2014. A new hybrid model is proposed, consisting of four major blocks: point forecasting, pre-filtering, quantile regression modeling and post-processing. This universal model structure enables independent development of a single block, without affecting performance of the rema...

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 especial importance of capital market in countries is undeniable in economic development via effective capital conduct and optimum resources allocation. Investment in capital market requires decision making in new stock exchanges, and   accessing information in the case of future status of capital market. Undoubtedly, nowadays most part of capital is exchanged via stock exchange all around ...

Abstract Long short-term memory (LSTM) models based on specialized deep neural network-based architecture have emerged as an important model for forecasting time-series. However, the literature does not provide clear guidelines design choices, which affect performance. Such choices include need pre-processing techniques such deseasonalization, ordering of input data, network size, batch and hor...

Flexible and dynamic supply chain network design problem has been studied by many researchers during past years. Since integration of short-term and long-term decisions in strategic planning leads to more reliable plans, in this paper a multi-objective model for a sustainable closed-loop supply chain network design problem is proposed. The planning horizon of this model contains multiple strate...