Abstract
Nickel is a vital strategic metal resource with commodity and financial attributes simultaneously, whose price fluctuation will affect the decision-making of stakeholders. Therefore, an effective trend forecast of nickel price is of great reference for the risk management of the nickel market’s participants; yet, traditional forecast methods are defective in prediction accuracy and applicability. Therefore, a prediction model of nickel metal price is proposed based on improved particle swarm optimization algorithm (PSO) combined with long-short-term memory (LSTM) neural networks, for higher reliability. This article introduces a nonlinear decreasing assignment method and sine function to improve the inertia weight and learning factor of PSO, respectively, and then uses the improved PSO algorithm to optimize the parameters of LSTM. Nickel metal’s closing prices in London Metal Exchange are sampled for empirical analysis, and the improved PSO-LSTM model is compared with the conventional LSTM and the integrated moving average autoregressive model (ARIMA). The results show that compared with the standard PSO, the improved PSO has a faster convergence rate and can improve the prediction accuracy of the LSTM model effectively. In addition, compared with the conventional LSTM model and the integrated moving average autoregressive (ARIMA) model, the prediction error of the LSTM model optimized by the improved PSO is reduced by 9% and 13%, respectively, which has high reliability and can provide valuable guidance for relevant managers.
Highlights
Nickel is a rare metal with outstanding physical and chemical properties
In order to verify the effectiveness of the long-short-term memory (LSTM) neural network model with improved particle swarm optimization (PSO) optimization, five prediction models are constructed based on the deep learning library Keras. ey are as follows: (1) LSTM model with only one hidden layer optimized by standard PSO algorithm (PSO-LSTM11), (2) LSTM model with only one hidden layer optimized by improved PSO algorithm (PSO-LSTM12), (3) LSTM model with two hidden layers optimized by standard PSO algorithm (PSO-LSTM21), (4) LSTM model with two hidden layers optimized by improved PSO algorithm (PSOLSTM22), and (5) conventional LSTM model
It shows that, compared with the standard PSO algorithm, the improved PSO algorithm can effectively improve the prediction accuracy of the LSTM model. e maximum and minimum relative errors of the LSTM model optimized by PSO are lower than those of the conventional LSTM model and the autoregressive integrated moving average model (ARIMA) model, indicating that using the PSO algorithm to optimize the LSTM model can effectively improve the prediction accuracy of the LSTM model
Summary
Nickel is a rare metal with outstanding physical and chemical properties. It is known as the “vitamin of the steel industry” and is the raw material of green batteries. 2. Related Research Theory e LSTM neural network can learn the complex association between features and tags, but its learning process is highly susceptible to time step, the number of hidden layers, and the number of nodes in each hidden layer. Related Research Theory e LSTM neural network can learn the complex association between features and tags, but its learning process is highly susceptible to time step, the number of hidden layers, and the number of nodes in each hidden layer These parameters are usually determined by manual adjustment, which increases the complexity of the operation process and may result in lower prediction accuracy [22]. E setting of this parameter helps the LSTM neural network to learn long-term dependency information within the time-series data to improve the accuracy of the prediction results. If the residual sequence is not white noise, it indicates that the currently constructed model does not fit all the valuable information in the sequence, and the parameters of the model need to be redetermined until the residual sequence is classified as white noise
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