Abstract
In this study, it was aimed to determine artificial neural network models with different architectures using artificial neural network (ANN) methods used in future prediction studies in recent times and forecast the sales quantities of industrial wood in Turkey with the help of models. The sales quantities of logs, mining poles, other industrial wood, pulpwood, fiber-chip wood, and the total of these five wood groups were analyzed separately. The data used in this study was obtained from the General Directorate of Forestry of Turkey and cumulative monthly data covering the period from January 2001 to December 2016 were used. The most suitable ANN models were determined using performance criteria such as mean absolute percentage error (MAPE), root mean square error (RMSE), and determination coefficient (R2). As a result, the R2 and MAPE values of the ANN models were found to be above 99% and below 6%, respectively. The ANNs can be used as a good tool in industrial wood sales forecasts.
Highlights
In situations where there are multiple options, individuals, institutions, and organizations that are decision makers become undecided about doing so. They must choose an option, which must be the best one among the multiple options given because making decision regarding future with the least errors will increase the security of policies and planning to be determined in the future and reduce the costs
When the GDP data in Turkey for the period between 2001 and 2016 were analyzed, it was seen that the sale data of industrial wood were close to the predicted values obtained with the artificial neural network (ANN) models
Turkey imports a vast majority of industrial wood from Russia
Summary
In situations where there are multiple options, individuals, institutions, and organizations that are decision makers become undecided about doing so. They must choose an option, which must be the best one among the multiple options given because making decision regarding future (making predictions) with the least errors will increase the security of policies and planning to be determined in the future and reduce the costs. There are many methods used for forecasting These methods can be qualitative or quantitative. Qualitative methods are used to predict the future with the help of previous information, whereas quantitative forecasting methods are methods used to explain the structure of the obtained data. One of the quantitative methods is the method included in the time series analysis (Can, 2009; Hamzaçebi, 2011)
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