Forecasting Commodity Prices Using Long Short-Term Memory Neural Networks
This work provides an incremental improvement in commodity price forecasting for financial analysts and traders by demonstrating the utility of forecast averaging.
This paper explores the use of Long Short-Term Memory (LSTM) neural networks for forecasting cotton and oil prices. While LSTMs did not systematically outperform traditional ARIMA models, an ensemble approach averaging forecasts from both methods reduced the Root Mean Squared Error (RMSE) by 0.21% for cotton compared to ARIMA and 21.49% compared to LSTM.
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.