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A Comparison of artificial neural network and time series models for timber price forecasting
The majority of the existing studies on timber price forecasting are based on ARIMA/SARIMA autoregressive moving average models, while vector autoregressive (VAR) and exponential smoothing (ETS) models have been employed less often. To date, timber prices in primary timber markets have not been forecasted with ANN methodology. This methodology was used only for forecasting lumber futures. Low-labor-intensive and relatively simple solutions that can be used in practice as a tool supporting decisions of timber market participants were sought. The present work sets out to compare RBF and MLP artificial neural networks with the Prophet procedure and with classical models (i.e., ARIMA, ETS, BATS, and TBATS) in terms of their suitability for forecasting timber prices in Poland. The study material consisted of quarterly time series of net nominal prices of roundwood (W0) for the years 2005–2021. MLP was found to be far superior to other models in terms of forecasting price changes and levels. ANN models exhibited a better fit to minimum and maximum values as compared to the classical models, which had a tendency to smooth price trends and produce forecasts biased toward average values. The Prophet procedure led to the lowest quality of projections. Ex-post error-based measures of prediction accuracy revealed a complex picture. The best forecasts for alder wood were obtained using the ETS model (with RMSE and MAE values of approx. 0.38 € m−3). ETS also performed well with respect to beech timber, although in this case BATS was just as good in terms of RMSE, while the difference between ETS and neural models amounted to as little as 0.64 € m−3. Birch timber prices were most accurately predicted with BATS and TBATS models (MAE 0.86 € m−3, RMSE 1.04 € m−3). The prices of the most popular roundwood types in Poland, i.e., Scots pine, Norway spruce, and oaks, were best forecasted using ANNs, and especially MLP models. Among the neural models for oak (MAE 4.74 € m−3, RMSE 8.09 € m−3), pine (MAE 2.21 € m−3, RMSE 2.83 € m−3), beech (MAE 2.31 € m−3, RMSE 2.70 € m−3), alder (MAE 1.88 € m−3, RMSE 2.40 € m−3), and spruce (MAE 2.44 € m−3, RMSE 2.58 € m−3), the MLP model was the best (the RBF model for birch). Of the seven models used to forecast the prices of six types of wood, the worst results were obtained for oak wood, while the best results were obtained for alder.
The use of forest biomass for energy purposes in selected european countries
The utilization of primary and secondary woody biomass resources, despite controversies, is being promoted to reduce dependence on fossil fuels and due to the need to diversify energy sources and ensure energy security in European Union countries. Forest biomass is one of the renewable and sustainable energy sources that can be used for electricity, heat, and biofuel production. In the context of the ongoing energy crisis in Europe, an attempt was made to analyze the production and consumption of woody biomass for energy purposes (fuel wood, chips, and pellets). Specifically, an analysis of similarities between European countries in terms of biomass utilization was conducted. The analysis was complemented by a forecast of primary biomass production in selected European countries. The similarity analysis was conducted using the Ward method. Artificial neural networks (ANNs), including multi-layer feedforward perceptron (MLP) and radial basis function (RBF) models, were used to predict fuelwood extraction. The study showed that woody biomass remains an important source of bioenergy in Europe, and its significance as a strategic resource guaranteeing energy security is likely to increase. Fuel wood harvesting in Europe generally shows an upward trend, particularly in the Czech Republic, Germany, Estonia, Denmark, and the UK. A decreasing trend was observed in France, Spain, Greece, and Cyprus. The analysis revealed differences between countries in terms of woody biomass consumption. The ANN-based forecasts of fuelwood supply generally showed an increase in primary biomass harvesting.
The Impact of Selected Market Factors on the Prices of Wood Industry By-Products in Poland in the Context of Climate Policy Changes
The objective of this study was to analyze price variability and the factors influencing the formation of monthly prices of by-products of the wood industry in Poland between October 2017 and January 2025. The analysis considered the impact of economic variables, including energy commodity prices (natural gas and coal) and industrial wood prices, on the pricing of wood industry by-products. The adopted approach enabled the identification of key determinants shaping the prices of these by-products. The effectiveness of two tree-based regression models—Random Forest (RF) and CatBoost (CB)—was compared in the analysis. Although RF offers greater interpretability and lower computational requirements, CB proved more effective in modeling dynamic, time-dependent phenomena. The results indicate that industrial wood prices exerted a weaker influence on by-product prices than natural gas prices, suggesting that the energy sector plays a leading role in shaping biomass prices. Coal prices had only a marginal impact on the biomass market, implying that changes in coal availability and pricing did not directly translate into changes in the prices of wood industry by-products. The growing role of renewable energy sources derived from natural gas and wood biomass is contributing to the emergence of a distinct market, increasingly independent of the traditional coal market. In Poland, due to limited access to alternative energy sources, biomass plays a critical role in the decarbonization of the energy sector.