Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Approach to Forecasting Commodity Futures Prices Using Machine Learning

Version 1 : Received: 1 October 2022 / Approved: 5 October 2022 / Online: 5 October 2022 (13:39:16 CEST)

How to cite: Jesus, G.S.D.; Hoppe, A.F.; Sartori, A.; Leithardt, V.R.Q. An Approach to Forecasting Commodity Futures Prices Using Machine Learning. Preprints 2022, 2022100043. https://doi.org/10.20944/preprints202210.0043.v1 Jesus, G.S.D.; Hoppe, A.F.; Sartori, A.; Leithardt, V.R.Q. An Approach to Forecasting Commodity Futures Prices Using Machine Learning. Preprints 2022, 2022100043. https://doi.org/10.20944/preprints202210.0043.v1

Abstract

This paper presents the development and implementation of a machine learning model to estimate the future price of commodities in the Brazilian market from technical analysis indicators. For this, two databases were obtained regarding the commodities sugar, cotton, corn, soybean and wheat, which were submitted to the steps of data cleaning, pre-processing and subdivision. From the pre-processed data, recurrent neural networks of the long short-term memory type were used to perform the prediction of data in the interval of 1 and 3 days ahead. These models were evaluated using mean squared error, obtaining an accuracy between 0.00010 and 0.00037 on the test data for 1 day ahead and 0.00015 to 0.00041 for 3 days ahead. However, based on the results obtained, it can be stated that the developed model obtained a good prediction performance for all commodities evaluated.

Keywords

Commodities; Long Short-Term Memory; Machine Learning; Neural Networks; Prediction; Technical analysis

Subject

Computer Science and Mathematics, Information Systems

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