Neural Networks for Predicting Prices of Sugarcane Derivatives

Silva, Natália; Siqueira, Igor Valadares; Okida, Sérgio; Stevan, Sérgio Luiz; Siqueira, Hugo Valadares

doi:10.1007/s12355-018-0648-5

Cited by 17 publications

(17 citation statements)

References 33 publications

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“…This speculation attracts investors whose purpose is not to hedge the oil output, but rather make profit stemming from the price movements of energy commodities. In order to predict cane derivatives prices of sugar, Silva et al ( 2019 ) propose the use of extreme learning machines.…”

Section: Discussionmentioning

confidence: 99%

Sugar Futures as an Investment Alternative During Market Turmoil: Case Study of 2008 and 2020 Market Drop

Babirath¹,

Schmitl²,

Sahatqija

et al. 2020

Sugar Tech

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In times of turbulent financial markets, investors all around the globe seek for opportunities protecting their portfolios from devastating losses. Historically, commodities were regarded as a safe haven providing sound returns which offset potential losses arising from dropping equity prices in times of market turmoil. While sugar would have provided a proper hedge against crashing equity markets during the initiation of the 2007 bear market and the onset financial crisis, sugar prices dropped likewise equity during the outbreak of COVID-19 and the consequent market shock. The goal of the paper is to elaborate on the differences in sugar price dynamics during the aforementioned economic disruptions by employing a multiple linear regression approach using data from the last quarter 2007 as well as the first quarter of 2019. The findings suggest that the behavioral differences stem from the deep link between oil and sugar prices. While oil did not influence the price of sugar during the outbreak of the financial crisis, it had tremendous influence on sugar prices during the outbreak of the corona crisis. Currently, sugar provides a substantial upside for an investor's portfolio since the demand and supply-side shock on oil prices due to corona crisis as well as the Saudi-Russian oil price war drove oil prices and consequently sugar prices to a historic low. Sugar futures provide the advantage of offering a smaller contract size compared to oil futures, and even though both commodities trade in contango as of March 2020, the sugar future curve is by far not as steep as the oils. Resultingly, investors benefit from lower rollover costs while prospering from a potential surge in oil prices.

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Section: Discussionmentioning

confidence: 99%

Sugar Futures as an Investment Alternative During Market Turmoil: Case Study of 2008 and 2020 Market Drop

Babirath¹,

Schmitl²,

Sahatqija

et al. 2020

Sugar Tech

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“…In 2011, Ribeiro and Oliveira [6] introduced a hybrid model built upon artificial neural networks (ANNs) and Kalman filter. In 2019, Silva1 et al [7] investigated ANNs, extreme learning machines (ELMs), and echo state networks (ESNs) for sugar price forecasting. However, one limitation of abovementioned three methods is they do not optimize the hyperparameter of neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…e effectiveness of the proposed approach is tested at the daily sugar price of London Sugar Futures. To fairly compare with the mainstream methods for sugar price forecasting, we build the deep neuron networks (DNNs) with multiple fully connected layers which is equal to models in [5][6][7] in the machine learning field and the ARIMA compared with [4]. e results are compared against other machine learning algorithms such as the support vector regression (SVR) machine [15,[26][27][28][29], the DNN, and traditional time series model ARIMA.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Sugar Price Forecasting Based on Time Series Decomposition

Zhang

Meng

Jin

et al. 2021

Mathematical Problems in Engineering

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Sugar price forecasting has attracted extensive attention from policymakers due to its significant impact on people’s daily lives and markets. In this paper, we present a novel hybrid deep learning model that utilizes the merit of a time series decomposition technology empirical mode decomposition (EMD) and a hyperparameter optimization algorithm Tree of Parzen Estimators (TPEs) for sugar price forecasting. The effectiveness of the proposed model was implemented in a case study with the price of London Sugar Futures. Two experiments are conducted to verify the superiority of the EMD and TPE. Moreover, the specific effects of EMD and TPE are analyzed by the DM test and improvement percentage. Finally, empirical results demonstrate that the proposed hybrid model outperforms other models.

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“…The main difference between them is the training process since, in ELM, it is merely and based on a closed-form solution (Miche et al, 2010). In many forecasting tasks, the ELM overcame other neural architectures and linear models (Silva et al, 2019;Siqueira et al, 2014;Siqueira et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

Belotti

Mendes

Leme

et al. 2021

Journal of Hydrology and Hydromechanics

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Several activities regarding water resources management are dependent on accurate monthly streamflow forecasting, such as flood control, reservoir operation, water supply planning, hydropower generation, energy matrix planning, among others. Most of the literature is focused on propose, compare, and evaluate the forecasting models. However, the decision on forecasting approaches plays a significant role in such models’ performance. In this paper, we are focused on investigating and confront the following forecasting approaches: i) use of a single model for the whole series (annual approach) versus using 12 models, each one responsible for predicting each month (monthly approach); ii) for multistep forecasting, the use of direct and recursive methods. The forecasting models addressed are the linear Autoregressive (AR) and Periodic Autoregressive (PAR) models, from the Box & Jenkins family, and the Extreme Learning Machines (ELM), an artificial neural network architecture. The computational analysis involves 20 time series associated with hydroelectric plants indicated that the monthly approach with the direct multistep method achieved the best overall performances, except for the cases in which the coefficient of variation is higher than two. In this case, the recursive approach stood out. Also, the ELM overcame the linear models in most cases.

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Neural Networks for Predicting Prices of Sugarcane Derivatives

Cited by 17 publications

References 33 publications

Sugar Futures as an Investment Alternative During Market Turmoil: Case Study of 2008 and 2020 Market Drop

Sugar Futures as an Investment Alternative During Market Turmoil: Case Study of 2008 and 2020 Market Drop

A Novel Hybrid Deep Learning Model for Sugar Price Forecasting Based on Time Series Decomposition

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

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