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Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point ... see more

extreme-point symmetric mode decomposition | extreme learning machine | grey wolf optimizer algorithm | carbon price forecasting

Energies , 2019

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A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

Guoqiang Sun, Tong Chen, Zhinong Wei, Yonghui Sun, Haixiang Zang and Sheng Chen

Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated o... see more

carbon price forecasting | variational mode decomposition (VMD) | spiking neural network (SNN) | partial autocorrelation function (PACF) | comprehensive evaluation criteria

Energies , 2016

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A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network

Bangzhu Zhu

Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble fore... see more

Energies , 2012

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Forecasting of Daily Gold Price using ARIMA-GARCH Hybrid Model

Sigit Setyowibowo,Mohamad As'ad,Sujito Sujito,Eni Farida

Gold is a multifunctional precious metal. Apart from being jewelry, gold is a form of investment. For this reason, the public or investors need to know the estimated daily gold price for transactions for the public or investors who want to invest or also ... see more

Jurnal Ekonomi Pembangunan , 2021

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SHORT TERM PRICE FORECASTING USING TREE BASED METHODS

A K Srivastava,D Singh,A S Pandey,Sanjay Kumar

In this paper, electricity price forecasting using J48, Random forest and Bagging are used to effectively forecast the electricity price. These models are obtained by recursively partitioning the data space and fitting a simple prediction model within eac... see more

COMPUSOFT: An International Journal of Advanced Computer Technology , 2019

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To the issue of forecasting price dynamics in the property valuation

O. N. Gnennyi

The problems of short-, medium- and long-term forecasting price dynamics are considered. The improvement of short-term forecasting techniques based on exponential smoothing is proposed. A modified autoregressive model of the first-order differences for th... see more

Nauka ta Progres Transportu , 2012

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Forecasting Gold Prices Based on Extreme Learning Machine

Kumar Chandar Sivalingam,Sumathi Mahendran,Sivanandam Natarajan

In recent years, the investors pay major attention to invest in gold market ecause of huge profits in the future. Gold is the only commodity which maintains ts value even in the economic and financial crisis. Also, the gold prices are closely elated with ... see more

Feed forward neural networks | Extreme Learning Machine | Gold price forecasting.

International Journal of Computers Communications & Control , 2016

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Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network

Ying-Yi Hong and Ching-Ping Wu

Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs) resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. Th... see more

locational marginal price | forecasting | principal component analysis

Energies , 2012

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Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Taiyong Li, Zhenda Hu, Yanchi Jia, Jiang Wu and Yingrui Zhou

Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However,... see more

crude oil prices | time series forecasting | ensemble empirical mode decomposition (EEMD) | sparse Bayesian learning (SBL) | energy forecasting

Energies , 2018

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