a r t i c l e i n f o Keywords: Subtractive clustering Adaptive network-based fuzzy inference system Technical indicators Adaptive learning Genetic algorithm Technical analysis is one of the useful forecasting methods to predict the future stock prices. For professional stock analysts and fund managers, how to select necessary technical indicators to forecast stock trends is important. Traditio...

In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential trading risks, and supporting efficient system planning operation. Unlike existing methods that only consider LMPs' temporal features, this paper tailors a spectral graph convolutional network (GCN) to greatly improve t...

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant price-setting market, nowadays. Nevertheless, became a complex signal due to its non-stationary, non-linearity, time-variant behavior. Consequently, variety of artificial intelligence techniques are proposed provide efficient method shor...

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,...

In this paper, we showed a method to forecast the daily stock price using neural networks and the result of the Neural Network forecast is compared with the Statistical forecasting result. Stock price prediction is one of the emerging field in neural network forecasting area. This paper also presents the Neural Networks ability to forecast the daily Stock Market Prices. Stock market prediction ...

It is very important to forecast electricity price in a deregulated electricity market for choosing the bidding strategy, and it is the most important signal for other players. It engulfs information for both customers and producers in order to maximize their profit. Thus, choosing the best method of price forecasting is a crucial task to have the most accurate forecast. In this paper the price...

Abstract Forecasting the price of chicken plays a crucial important role in the poultry raising industry because it is beneficial to maximize the profit and minimize the risk. Its goal is to accurately predict the price in future based on the data obtained. ARMA is the classical time series prediction method and wavelet transform is well known to work well for reducing the noise of the data. In...

Since gold prices influence international economic and monetary systems, numerous studies have been conducted to forecast prices. Nonetheless, employing the linear relationship method usually fail explain change in pattern of price. This study introduces a new paradigm that incorporates association rules long short-term memory (LSTM) as nonlinear-based method. For simulation, proposed was analy...

conventionally, regression and time series analyses have been employed in modeling water demand forecasts. in recent years, the relatively new technique of neural networks (nns) has been proposed as an efficient tool for modeling and forecasting. the objective of this study is to investigate the relatively new technique of gmdh – type neural networks for the use of forecasting long – term urban...