Forests · 2021 · 17 citations · 12 references
ComputingData ScienceForestryForecastingMachine LearningTime Series AnalysisDeep LearningForecast Error AnalysisForecast EvaluationTime Series ForecastingTime Series ModelingTime Series PredictionCross-validationForest Product Price ForecastingLong Short-term Memory NetworksLumber Market AnalysisNeural ForecastingNeural Time Series ForecastingPredictive Price ModelingRecurrent Neural Network Forecasting
This manuscript confirms the feasibility of using a long short-term memory (LSTM) recurrent neural network (RNN) to forecast lumber stock prices during the great and Coronavirus disease 2019 (COVID-19) pandemic recessions in the USA. The database was composed of 5012 data entries divided into recession periods. We applied a timeseries cross-validation that divided the dataset into an 80:20 training/validation ratio. The network contained five LSTM layers with 50 units each followed by a dense output layer. We evaluated the performance of the network via mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) for 30, 60, and 120 timesteps and the recession periods. The metrics results indicated that the network was able to capture the trend for both recession periods with a remarkably low degree of error. Timeseries forecasting may help the forest and forest product industries to manage their inventory, transportation costs, and response readiness to critical economic events.
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