Enhancing Currency, Commodity and Energy Price Forecasting Using the LSTM Model: A Case Study of EUR/NZD, GAS and SUGAR Prices

Abstract

Forecasts from machine and deep learning models are vital for traders and investors in the global financial markets. Many different forecasting methods rely on technical patterns. In this study, the LSTM model based on candlesticks and financial variables was used to improve trading forecasts of different types. Japanese candlesticks are among the most widely used tools for evaluating financial markets. Therefore, these candlesticks, which show price patterns and differences between buying and selling, provide important data for predicting future price fluctuations. A 15-minute candlestick or 15-minute frame is used. The model showed excellent performance in predicting currency rates (EUR/NZDUSD), with an accuracy based on mean square error (MSE = 1.377e-07). The model also showed better accuracy in predicting sugar prices compared to other models, reaching (MSE = 1.419836). The same results were obtained with the GAS model, where the value was (MSE = 0.000173). This superior performance of the model indicates its ability to generate historical patterns and use them effectively in forecasting financial markets. These results provide promising opportunities for traders and investors to make more guided and intelligent investment decisions based on future trends based on these patterns. By using historical patterns and financial data, LSTM's deep learning model shows exceptional predictive performance. It outperforms traditional machine learning methods such as XGBoost. XGBoost achieved a score on the EUR/NZDUSD exchange rate (MSE = 9.537e-07). The error rate for the presented model is considered to be high. This confirms the success of the represented approach and its ability to enable traders and investors to make more informed and strategic decisions. This ultimately contributes to improving trading conditions and investment outcomes in global financial markets.