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 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 ACO-BPNN.

中文翻译：

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金属商品价格的混合预测模型：经验模型评估

在市场不断波动的情况下，对完美商品预测做出适当的决策，加剧了对有效方法的需求。这项研究的主要目的是在反向传播神经网络（BPNN）上应用不同的优化算法，例如常规粒子群优化（PSO），蝙蝠算法（BAT）和蚁群优化（ACO）算法，以提高预测和预测的准确性。最小化错误。本文提出了一种利用PSO算法训练BPNN并预测商品收盘价的波动率预测模型。与BPNN，BAT-BPNN和ACO-BPNN相比，所提出的PSO-BPNN模型被认为是最佳预测模型。实验是根据五个公开的金属数据集（金，银，铅，铝，和铜）来预测挑战有效性的这五种金属的价格回报波动性。这里有三个技术指标和四个过滤器，例如；移动平均会聚/散度（MACD），威廉姆斯％R（W％），波林格（B），最小均方（LMS），有限脉冲响应（FIR），卡尔曼和递归最小二乘（RLS）已用于提供其他训练和测试分类器的自由度。从实验结果分析发现，与BPNN，BAT-BPNN和ACO-BPNN相比，提出的PSO-BPNN产生了可观的输出。Kalmanand递归最小二乘（RLS）已用于提供额外的自由度来训练和测试分类器。从实验结果分析发现，与BPNN，BAT-BPNN和ACO-BPNN相比，提出的PSO-BPNN产生了可观的输出。Kalmanand递归最小二乘（RLS）已用于提供额外的自由度来训练和测试分类器。从实验结果分析发现，与BPNN，BAT-BPNN和ACO-BPNN相比，提出的PSO-BPNN产生了可观的输出。