Forecasting commodity prices using long-short-term memory neural networks

Abstract

This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods. Our results show that machine learning methods fit reasonably well the data but do not outperform systematically classical methods such as Autoregressive Integrated Moving Average (ARIMA) models in terms of out of sample forecasts. However, averaging the forecasts from the two type of models provide better results compared to either method. Compared to the ARIMA and the LSTM, the Root Mean Squared Error (RMSE) of the average forecast was 0.21 and 21.49 percent lower respectively for Cotton. For Oil the forecast averaging does not provide improvements in terms of RMSE. We suggest using a forecast averaging method and extending our analysis to a wide range of commodity prices.

Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

“…Such an observation has been found in other studies as well. Ly et al (2021) also noted in their study that the LSTM did not perform better than the traditional ARIMA model in single‐step ahead forecasting the cotton and oil prices. Also, in the study of Sun and Jin (2022), the ARIMA model achieved better results in 1‐h ahead to forecast the wind speed.…”

“…Multivariate variables are necessary to strengthen the performance of prediction models. Commonly used deep learning models include ANN (Ayankoya et al, 2016) and Long Short‐Term Memory (LSTM) (Ly et al, 2021; Rasheed et al, 2021). These models have good predictive power in single‐step forecasting.…”

“…A set of environmental variables have been tested to predict the grain yield (Ayankoya et al, 2016; Ly et al, 2021; Rasheed et al, 2021), however, the snow‐related indicators were not yet been included in predictive models. Snow is an essential resource, providing water and climate regulation for communities near mountainous areas (Qin et al, 2020; Sturm et al, 2017).…”

Machine Learning Challenges and Opportunities in the African Agricultural Sector -- A General Perspective

“…The systematic comparison between traditional regression-based techniques and ML results showed that it was premature at the time to assume that the algorithms would outperform statistical methods. However, with improvements in mathematical modeling, data availability, and greater computing capacities, ML algorithms now yield satisfactory accuracy in some areas, even though regression-based techniques can still provide better results in specific contexts (Ly et al, 2021).…”

Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices

“…As food is essential to people's day-to-day life, the accuracy in price prediction as well as knowing the prices in advance are indispensable to properly guide agricultural production, make a correct balance between supply and demand, increase farmer's income, assist the farmers to plan their next crop, and to help the government, farmers, business people in agriculture and consumers to get a clear market awareness, devising business plans, finetuning individuals own finances, and reducing the risks and uncertainties to be handled (Zhang et al, 2020). Though forecasting of agricultural price is pertinent and considered as paramount for many economic actors (Ly, Traore, & Dia, 2021), as literature confirms research has not reaped the benefits of deep learning-based agricultural price forecasting and remains to be an unsolved problem on date (Sagheer & Kotb, 2019;Paroissien, 2020).…”