时间：2023年2月26日9：30 – 10：30

报告地点：民主楼118

报告人：刘一顺

报告标题：A Novel Zinc Price Forecasting Method Based on Multi-Factor Selection and LSTM Network

报告摘要：

Zinc is an indispensable base material for the development of national economy and the construction of national defense industry, and the price forecasting is of great significance for investors, policy makers and researchers. Considering the complexity, dynamic and strong nonlinearity of zinc price changes, and it is usually affected by a variety of external factors, it is difficult to obtain a satisfactory forecasting effect only by analyzing the underlying pattern of historical price data changing. To solve the aforementioned problem, a novel zinc price forecasting method based on factor selection and long short-term memory network is proposed. Compared with other state-of-the-art methods from value and direction prediction accuracy and fitting ability, the proposed model has superior performance for zinc price forecasting.

报告标题：基于多因素选择和LSTM网络的锌价格预测新方法

报告摘要：

锌是国民经济发展和国防工业建设不可缺少的基础材料，其价格预测对投资者、政策制定者和研究人员具有重要意义。考虑到锌价格变化的复杂性、动态性和强非线性，且通常受到多种外部因素的影响，仅通过分析历史价格数据变化的潜在模式很难获得令人满意的预测效果。针对上述问题，提出了一种基于因素选择和长短记忆网络的锌价格预测方法。实验结果表明，无论从数值预测精度还是方向预测精度，本文所提出的模型对锌价预测具有优越的性能。