Forecasting commodity prices using long-short-term memory neural networks
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...
| Autores principales: | , , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/143474 |
| _version_ | 1855524609296695296 |
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| author | Ly, Racine Traoré, Fousseini Dia, Khadim |
| author_browse | Dia, Khadim Ly, Racine Traoré, Fousseini |
| author_facet | Ly, Racine Traoré, Fousseini Dia, Khadim |
| author_sort | Ly, Racine |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Artículo preliminar |
| id | CGSpace143474 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1434742025-12-02T21:02:52Z Forecasting commodity prices using long-short-term memory neural networks Ly, Racine Traoré, Fousseini Dia, Khadim models forecasting neural networks commodities cotton machine learning networks oils prices 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. 2021-02-01 2024-05-22T12:14:23Z 2024-05-22T12:14:23Z Working Paper https://hdl.handle.net/10568/143474 en Open Access application/pdf International Food Policy Research Institute Ly, Racine; Traore, Fousseini; and Dia, Khadim. 2021. Forecasting commodity prices using long-short-term memory neural networks. IFPRI Discussion Paper 2000. Washington, DC: International Food Policy Research Institute. https://doi.org/10.2499/p15738coll2.134265. |
| spellingShingle | models forecasting neural networks commodities cotton machine learning networks oils prices Ly, Racine Traoré, Fousseini Dia, Khadim Forecasting commodity prices using long-short-term memory neural networks |
| title | Forecasting commodity prices using long-short-term memory neural networks |
| title_full | Forecasting commodity prices using long-short-term memory neural networks |
| title_fullStr | Forecasting commodity prices using long-short-term memory neural networks |
| title_full_unstemmed | Forecasting commodity prices using long-short-term memory neural networks |
| title_short | Forecasting commodity prices using long-short-term memory neural networks |
| title_sort | forecasting commodity prices using long short term memory neural networks |
| topic | models forecasting neural networks commodities cotton machine learning networks oils prices |
| url | https://hdl.handle.net/10568/143474 |
| work_keys_str_mv | AT lyracine forecastingcommoditypricesusinglongshorttermmemoryneuralnetworks AT traorefousseini forecastingcommoditypricesusinglongshorttermmemoryneuralnetworks AT diakhadim forecastingcommoditypricesusinglongshorttermmemoryneuralnetworks |