Use of Artificial Neural Networks and Arima to Forecasting Consumption Sawnwood of Pinus sp. in Brazil
The objective of this study was to analyze the application of an artificial neural networks model and an ARIMA model to predict the consumption of sawnwood of pine. For this, we use real and secondary data collected and obtained from a historical data source, corresponding to the period
from 1997 to 2016, which were later tested to generate the forecast models. Based on economic and statistical criteria, six explanatory variables were used to fit the best model. The choice of the model was made based on Mean Squared Error, Mean Absolute Error, Theil U metric, Percentage Error
of Forecast and Akaike value information criterion. The results indicated that the models generated through the ARIMA model presented better performance when compared to the artificial neural network. The best adjusted model estimated a reduction of 1.33% in consumption of sawnwood of pine
in Brazil for the period between 2017 and 2020.
Keywords: ECONOMETRICS; FORECAST; FORECASTING MODELS; TIME SERIES; WOOD PRODUCTS
Document Type: Research Article
Publication date: 01 March 2019
- The International Forestry Review is a peer-reviewed scholarly journal that publishes original research and review papers on all aspects of forest policy and science, with an emphasis on issues of transnational significance. It is published four times per year, in March, June, September and December. Theme editions are a regular feature and attract a wide audience.
The IFR is part of The Global Forest Information Service - GFIS
International Forestry Review has an Impact Factor of 1.705 - Editorial Board
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