立木
人工神经网络
计量经济学
均方误差
平均绝对误差
经济
价值(数学)
计算机科学
系列(地层学)
计量经济模型
统计
数学
人工智能
机器学习
农业经济学
古生物学
生物
作者
Sabhyata Lamichhane,Bin Mei,Jacek P. Siry
标识
DOI:10.1016/j.forpol.2023.103028
摘要
We conducted a comparative analysis of the predictive ability of classical econometric models and artificial neural networks (ANNs) for pine sawtimber stumpage prices across 22 TimberMart-South regions in the US using quarterly prices from 1976 to 2022. We evaluated model accuracy via root mean square error and mean absolute percentage error metrics and employed the modified Diebold-Mariano test to determine if there was a significant difference in forecast accuracy between the two models. Our findings demonstrate that ANNs outperform classical models in predicting turning points, whereas classical models tend to smooth price trends and produce forecasts that are biased toward the average value. This study provides a basis for predicting timber prices in the southern timber market using ANN models and contributes to ongoing discussions on the effectiveness of machine learning algorithms in generating precise forecasts within the forest industry. The findings can help timberland investors to make informed business decisions in the timber market.
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