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...

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,...

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,...

Numerous research have been done to predict cryptocurrency prices since affect global economic and monetary systems. However, investigations using linear connection approaches technical analysis indicators frequently fall short of providing an explanation for changes in the pattern BitCoin pricing. This paper is proposed study time-varying parameters with long short-term memory (LSTM). The inve...

Mastering the underlying characteristics of carbon price changes can help governments formulate correct policies to keep efficient operation of carbon markets, and investors take effective measures to evade their investment risks. Empirical mode decomposition (EMD), a self-adaption data analysis approach for nonlinear and non-stationary time series, can accurately explain the formation mechanis...

This paper presents a novel hybrid method for short-term load forecasting. The system comprises of two artificial neural networks (ANN), assembled in a hierarchical order. The first ANN is a multilayer perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor...