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<p>A Hybridized Forecasting Model for Metal Commodity Prices: <span style="white-space: pre;"> </span>An Empirical Model Evaluation</p>

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Title Statement <p>A Hybridized Forecasting Model for Metal Commodity Prices: <span style="white-space: pre;"> </span>An Empirical Model Evaluation</p>
 
Added Entry - Uncontrolled Name Parida, Nirjharinee ; Department of Comp. Sc. and Engg., Siksha ‘O’Anusandhan Deemed to be University, Odisha 751 030, India
Mishra, Debahuti ; Department of Comp. Sc. and Engg., Siksha ‘O’Anusandhan Deemed to be University, Odisha 751 030, India
Das, Kaberi ; Department of Comp. Sc. and Engg., Siksha ‘O’Anusandhan Deemed to be University, Odisha 751 030, India
Rout, Narendra Kumar; Department of Comp. application, National Institute of Technology, Raipur (C.G) 492 010, India
Panda, Ganapati ; Department of Electr. & Telecom. Engg., C. V. Raman Global University, Odisha 752 054, India
 
Uncontrolled Index Term Ant colony optimization; BPNN; Commodity market forecasting; PSO
 
Summary, etc. <p>Appropriate decision on perfect commodity prediction under market’s constant fluctuations intensifies the need for efficient methods. The main objective of this study is to apply different optimization algorithms such as conventional particle swarm optimization (PSO), bat algorithm (BAT) and ant colony optimization (ACO) algorithms on back propagation neural network (BPNN) to enhance the accuracy of prediction and minimize the error. In this paper, a model has been proposed for volatility forecasting using PSO algorithm to train the BPNN and predict the commodities’ closing price. The proposed PSO-BPNN model is considered as best forecasting model compared to BPNN, BAT-BPNN and <br /> ACO-BPNN. The experiment has been carried out upon five publicly available metal datasets (gold, silver, lead, aluminium, and copper) to forecast the price return volatility of those five metals challenging the effectiveness. Here, three technical indicators and four filters, such as; moving average convergence/divergence (MACD), williams %R (W%), bollinger (B), least mean squares (LMS), finite impulse response (FIR), Kalmanand recursive least square (RLS) have been applied for providing an additional degree of freedom to train and test the classifiers. From the experimental result analysis it has been found that the proposed PSO-BPNN produces promising output while comparing with BPNN, BAT-BPNN and <br /> ACO-BPNN.</p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2021-01-02 22:21:44
 
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http://op.niscair.res.in/index.php/JSIR/article/view/43592
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 79, ##issue.no## 10 (20)
 
Language Note en