ANN, LSTM, and SVR for Gold Price Forecasting

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.