ANN, LSTM, and SVR for Gold Price Forecasting

Jiacheng Yang, Denis De Montigny, P. Treleaven
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引用次数: 3

Abstract

This paper investigates a series of machine learning models (e.g. ANN, LSTM, SVR) to predict gold prices according to traditional indices, emerging indicators, commodities, and historical price time series of gold. In our approach, three machine learning algorithms, Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR), are applied to build the models that forecast the gold price. The dataset for this research is a time-series from 1st January 2017 to 31st December 2020, containing two major indices in the US (S&P 500 and DJI), two popular cryptocurrencies (BTC and ETH), two commodities (silver and crude oil), USD index (United States Dollar against Euro), and the gold prices (historical price and volatility) [24]. The evaluation benchmarks are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). In the first stage, a comparative analysis is applied to three models. In the second stage, the assessment of the impact of cryptocurrency on the models is demonstrated. It was observed that the SVR model outperforms the other two models, and our result indicates that the additional data of cryptocurrencies has a positive impact on all three models.
基于神经网络、LSTM和SVR的黄金价格预测
本文研究了基于传统指数、新兴指标、大宗商品和黄金历史价格时间序列的一系列机器学习模型(如ANN、LSTM、SVR)来预测黄金价格。在我们的方法中,三种机器学习算法,人工神经网络(ANN),长短期记忆(LSTM)和支持向量回归(SVR),应用于构建预测黄金价格的模型。本研究的数据集是从2017年1月1日到2020年12月31日的时间序列,包含美国的两个主要指数(标准普尔500指数和道琼斯指数),两种流行的加密货币(比特币和ETH),两种商品(白银和原油),美元指数(美元兑欧元)和黄金价格(历史价格和波动性)[24]。评估基准是平均绝对误差(MAE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)。在第一阶段,对三个模型进行了比较分析。在第二阶段,演示了加密货币对模型的影响评估。观察到SVR模型优于其他两个模型,我们的结果表明,加密货币的附加数据对所有三个模型都有积极的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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