Forecasting commodity prices using long short term memory neural networks is popular PDF and ePub book, written by Ly, Racine in 2021-02-10, it is a fantastic choice for those who relish reading online the Political Science genre. Let's immerse ourselves in this engaging Political Science book by exploring the summary and details provided below. Remember, Forecasting commodity prices using long short term memory neural networks can be Read Online from any device for your convenience.
Forecasting commodity prices using long short term memory neural networks Book PDF Summary
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.
Detail Book of Forecasting commodity prices using long short term memory neural networks PDF
- Author : Ly, Racine
- Release : 10 February 2021
- Publisher : Intl Food Policy Res Inst
- ISBN : 978186723xxxx
- Genre : Political Science
- Total Page : 26 pages
- Language : English
- PDF File Size : 13,5 Mb
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