用LSTM+MLP+灰狼优化预测金价，三月收益达171%。
Gold Price Prediction Using Long Short-Term Memory and Multi-Layer Perceptron with Gray Wolf Optimizer
- 双时间尺度LSTM结合MLP融合预测，灰狼算法优化网络结构。
- 日度收盘价预测MAE仅0.21美元，月度预测误差22.23美元。
- 适合量化交易者，可直接用于实盘策略部署。
全球黄金市场受宏观经济与政治因素复杂影响，精准预测极具挑战。本文提出一种基于人工智能的每日与月度金价预测模型：两个LSTM网络分别处理日频与月频数据，结果输入MLP网络生成最终预测，涵盖最高价、最低价与收盘价。通过灰狼优化（GWO）算法优化各层神经元数量，以最小化均方根误差（RMSE）。模型基于2010至2021年数据训练，包含发达经济体的宏观、能源、股市及货币指标。实验显示，日度收盘价预测平均绝对误差（MAE）为0.21美元，月度预测误差为22.23美元。据此制定的交易策略在三个月内实现171%收益率。
原文摘要 · Abstract (English)
The global gold market, by its fundamentals, has long been home to many financial institutions, banks, governments, funds, and micro-investors. Due to the inherent complexity and relationship between important economic and political components, accurate forecasting of financial markets has always been challenging. Therefore, providing a model that can accurately predict the future of the markets is very important and will be of great benefit to their developers. In this paper, an artificial intelligence-based algorithm for daily and monthly gold forecasting is presented. Two Long short-term memory (LSTM) networks are responsible for daily and monthly forecasting, the results of which are integrated into a Multilayer perceptrons (MLP) network and provide the final forecast of the next day prices. The algorithm forecasts the highest, lowest, and closing prices on the daily and monthly time frame. Based on these forecasts, a trading strategy for live market trading was developed, according to which the proposed model had a return of 171% in three months. Also, the number of internal neurons in each network is optimized by the Gray Wolf optimization (GWO) algorithm based on the least RMSE error. The dataset was collected between 2010 and 2021 and includes data on macroeconomic, energy markets, stocks, and currency status of developed countries. Our proposed LSTM-MLP model predicted the daily closing price of gold with the Mean absolute error (MAE) of $ 0.21 and the next month's price with $ 22.23.
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