There have been many improvements and advancements in the application of neural networks in the mining industry. In this study, two advanced deep learning neural networks called recurrent neural network (RNN) and autoregressive integrated moving average (ARIMA) were implemented in the simulation and prediction of limestone price variation. The RNN uses long short-term memory layers (LSTM), dropout regularization, activation functions, mean square error (MSE), and the Adam optimizer to simulate the predictions. The LSTM stores previous data over time and uses it in simulating future prices based on defined parameters and algorithms. The ARIMA model is a statistical method that captures different time series based on the level, trend, and seasonality of the data. The auto ARIMA function searches for the best parameters that fit the model. Different layers and parameters are added to the model to simulate the price prediction. The performance of both network models is remarkable in terms of trend variability and factors affecting limestone price. The ARIMA model has an accuracy of 95.7% while RNN has an accuracy of 91.8%. This shows that the ARIMA model outperforms the RNN model. In addition, the time required to train the ARIMA is than that of the RNN. Predicting limestone prices may help both investors and industries in making economical and technical decisions, for example, when to invest, buy, sell, increase, and decrease production.

中文翻译：

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使用 LSTM 和 ARIMA 模拟和预测石灰石价格变化

神经网络在采矿业的应用已经有了很多改进和进步。在这项研究中，在石灰石价格变化的模拟和预测中实施了两个先进的深度学习神经网络，称为循环神经网络 (RNN) 和自回归综合移动平均 (ARIMA)。RNN 使用长短期记忆层 (LSTM)、dropout 正则化、激活函数、均方误差 (MSE) 和 Adam 优化器来模拟预测。LSTM 随着时间的推移存储以前的数据，并根据定义的参数和算法将其用于模拟未来的价格。ARIMA 模型是一种统计方法，它根据数据的水平、趋势和季节性捕获不同的时间序列。自动 ARIMA 函数搜索适合模型的最佳参数。模型中添加了不同的层和参数来模拟价格预测。两种网络模型在趋势变化和影响石灰石价格的因素方面的表现都非常出色。ARIMA 模型的准确率为 95.7%，而 RNN 的准确率为 91.8%。这表明 ARIMA 模型优于 RNN 模型。此外，训练 ARIMA 所需的时间也比 RNN 的要长。预测石灰石价格可以帮助投资者和行业做出经济和技术决策，例如，何时投资、购买、出售、增加和减少产量。7%，而 RNN 的准确率为 91.8%。这表明 ARIMA 模型优于 RNN 模型。此外，训练 ARIMA 所需的时间也比 RNN 的要长。预测石灰石价格可以帮助投资者和行业做出经济和技术决策，例如，何时投资、购买、出售、增加和减少产量。7%，而 RNN 的准确率为 91.8%。这表明 ARIMA 模型优于 RNN 模型。此外，训练 ARIMA 所需的时间也比 RNN 的要长。预测石灰石价格可以帮助投资者和行业做出经济和技术决策，例如，何时投资、购买、出售、增加和减少产量。