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

Racine Ly, Traoré, Fousseini and Khadim Dia

No 2000, IFPRI discussion papers from International Food Policy Research Institute (IFPRI)

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 with the data but do not outperform systematically classical methods such as Autoregressive Integrated Moving Average (ARIMA) or the naïve 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.

Keywords: models; forecasting; neural networks; commodities; cotton; machine learning; networks; oils; prices (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-for
References: Add references at CitEc
Citations:

Downloads: (external link)
https://hdl.handle.net/10568/143474

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fpr:ifprid:2000

Access Statistics for this paper

More papers in IFPRI discussion papers from International Food Policy Research Institute (IFPRI) Contact information at EDIRC.
Bibliographic data for series maintained by (ifpri-library@cgiar.org).