PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

The application of trend estimation model in predicting the average selling price of timber

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article analyzes the possibility of adopting trend estimation model to predict the average selling price of timber (CGUS). The study used information about the average selling prices of timber in chosen periods (2006-2017). The data concerning the actual CGUS was used to create a trend estimation model. The models and CGUS predictions were conducted based on three different time series encompassing 5-year periods. The predicted (CGUS) trend estimation in particular years was requested based on extrapolation, which exceeded the accepted set of information used in the study to create a trend estimation model. On the basis of the conducted study it was ascertained that the method of modeling linear trend estimation should be adopted in the price prediction process. The error assessment with which the linear function formulas are burdened, it was noticed that the value of the coefficient of residual variation was between 4.40% and 7.82%. It was also noticed that the linear modeling of CGUS trend estimation, despite unfavorable values of coefficient of determination and convergence, to some extent, can be viewed as an assistance tool in the decisionmaking process in the scope of predicting the height of the analyzed price. This view was supported by the achieved predictions which were verified with the actual prices of timber. The price difference between the actual and the predicted one was between -1.59 PLN to 2.27 PLN, and in relative terms the predictive error was between 0.83 to 1.15%. In our opinion the presented research process can constitute a reference point as a comparative element to verify the results for other, new price prediction models. The process of modeling timber prices should be extended by other predicators which are connected with forest market chain.
Rocznik
Strony
147--159
Opis fizyczny
Bibliogr. 36 poz., tab.
Twórcy
  • Poznan University of Life Sciences, Poznan, Poland
  • Poznan University of Life Sciences, Poznan, Poland
Bibliografia
  • Adamowicz K., Kaciunka H. [2014]: Ocena tempa zmian kosztów produkcji drewna “przy pniu” i cen surowca drzewnego w latach 2001-2009 na przykładzie Regionalnej Dyrekcji Lasów Państwowych w Zielonej Górze (Assessing the cost changes in wood production "near the trunk" and the prices of wood raw material in 2001-2009 on the example of the Regional Directorate of State Forests in Zielona Góra). Leśne Prace Badawcze 75: 55-60. DOI: 10.2478/frp-2014-0006
  • Adamowicz K., Noga T. [2014]: Multivariate analysis of bankruptcy in companies in the wood sector. Sylwan 158: 643-650
  • Bowerman B.L., O’Connell R.T., Koehler A.B. [2005]: Forecasting, time series, and regression: An applied approach. Thomson Brooks/Cole
  • Chai J., Wang Y., Wang S., Wang Y. [2019]: A decomposition-integration model with dynamic fuzzy reconstruction for crude oil price prediction and the implications for sustainable development. Journal of Cleaner Production 229: 775-786. DOI:10.1016/J.JCLEPRO.2019.04.393
  • Chou J.S., Ngo N.T. [2016]: Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns. Applied Energy. DOI:10.1016/j.apenergy.2016.05.074
  • Cieślak M. [2008]: Prognozowanie gospodarcze: metody i zastosowania (Economic forecasting: methods and applications). Wydawnictwo Naukowe PWN
  • Dittmann P. [1996]: Metody prognozowania sprzedaży w przedsiębiorstwie (Sales forecasting methods in companies). Wydawnictwo Akademii Ekonomicznej im. Oskara Langego
  • Du Y. [2018]: Application and analysis of forecasting stock price index based on combination of ARIMA model and BP neural network. In: Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018. pp. 2854-2857. DOI:10.1109/CCDC.2018.8407611
  • Duc Cao M., Kumar Purohit P., Bauer L., Faseruk A. [2015]: How effective are quantitative methods in forecasting crude oil prices?. Journal of Financial Management and Analysis 28: 1-10
  • Gołos P., Zając S. [2008]: The role of forestry in the socio-economic development of Poland’s agricultural region (input-output analysis). Folia Forestalia Polonica/Seria A 49-50: 69-80
  • Grladinović T., Oblak L., Hitka M. [2007]: Production management information system in wood processing and furniture manufacture. Drvna Industrija 58 [3]: 141-146
  • Grzegorzewska E., Stasiak-Betlejewska R. [2014]: The influence of global crisis on financial liquidity and changes in corporate debt of the furniture sector in Poland. Drvna Industrija 65 [4]: 315-322. DOI: 10.5552/drind.2014.1342
  • Kaliszewski, A., Młynarski, W. [2014]: Direct costs and sources of financing of nature conservation and biodiversity protection in forest districts in the Mazowieckie Province. Sylwan 158: 491-498
  • Kocel J. [2010]: Podstawy metodyczne prognozy finansowo-gospodarczej dla Lasów Państwowych (Methodological basis of the financial and economic forecast for the State Forests). Sylwan 154, 41-51
  • Kowalik S., Herczakowska J. [2010]: Analiza i prognoza cen ropy naftowej na rynkach międzynarodowych (Analysis and forecast of crude oil prices on international markets). Polityka Energetyczna 13: 253-263
  • McNally S., Roche J., Caton S. [2018]: Predicting the price of bitcoin using machine learning. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE: 339-343
  • Mondal P., Shit L., Goswami S. [2014]: Study of effectiveness of time series modelling (Arima) in forecasting stock prices. International Journal of Computational Science and Engineering 4 [2]: 13-29. DOI:10.5121/ijcsea.2014.4202
  • Omar H., Hoang V.H., Liu D.R. [2016]: A hybrid neural network model for sales forecasting based on ARIMA and search popularity of article titles. Computational Intelligence and Neuroscience 4:1-9. DOI: 10.1155/2016/9656453
  • Paschalis-Jakubowicz P. [2012]: Uwarunkowania strategii rozwoju Lasów Państwowych (Conditions for the development strategy of the State Forests). Centrum Informacyjne Lasów Państwowych
  • Paschalis-Jakubowicz P. [2011]: Theoretical basis and implementation of the idea of sustainable development in forestry. Problems of Sustainable Development 6 [2]: 101-106
  • Płotkowski L. [2004]: Key issues in the forest sector today. Sylwan 148, 22-36
  • Popławski T. [2006]: Zastosowanie wybranych technik prognostycznych do krótkoterminowych prognoz cen energii elektrycznej na Towarowej Giełdzie Energii (Application of selected forecasting techniques to short-term forecasts of electricity prices on the Polish Power Exchange). Polityka Energetyczna 9: 143-155
  • Ratajczak E. [2011] Popyt na drewno w Polsce-zmiany strukturalne oraz możliwości zaspokojenia (Demand for wood in Poland – structural changes and possibilities of fulfillment). In: Strategy for the development of forests and forestry in Poland until 2030. In: Strategia rozwoju lasów i leśnictwa w Polsce do roku 2030. Zimowa Szkoła Leśna przy Instytucie Badaw. Leśnictwa III Ses. 15-17
  • Ripken H. [2000]: Entwicklung der Personalkosten und des Personal-abbaus in den deutschen Landesforstverwaltungen. Forst und Holz 55: 643-647
  • Shao Y.E., Dai J.T. [2018]: Integrated feature selection of ARIMA with computational intelligence approaches for food crop price prediction. Complexity. DOI: 10.1155/2018/1910520
  • Soares N.S., da Silva M.L., de Carvalho K.H.A., de Rezende J.L.P., de Lima J.E. [2010]: Eucalyptus spp. Wood price forecasting model. Cerne. DOI: 10.1590/S0104-77602010000100005
  • Suchodolski P., Idzik M. [2018]: Identyfikacja i ocena zmienności cen drewna w nadleśnictwie Płock (Identification and evaluation of wood price volatility in the Płock forest district). Wiadomości Statystyczne: 41-55
  • Suchomel J., Gejdos M. [2007]: Analysis of wood resources and price comparation in Slovakia and selected countries. In: Proceedings of the. 4th International Conference Woodworking Technique: 143-152\r339
  • Szramka H., Adamowicz K. [2017]: Kierunki modyfikacji statusu Lasów Państwowych w Polsce (Directions for modifying the status of the State Forests in Poland). Sylwan 161: 355-364
  • Szramka H., Starosta Grala M., Adamowicz K. [2016]: Leśnictwo w sektorowym rozwoju gospodarki w Polsce (Forestry in the sectoral development of the economy in Poland). Sylwan 160: 416-423
  • Szyndler J. [2007]: Racjonalizacja struktury organizacyjnej LP (Rationalization of the organizational structure of the State Forests). Las Polski: 20-22
  • Teplická K., Čulková K., Železník O. [2015]: Application of bayess principle optimum – Optimization model for managerial decision and continual improvement. Polish Journal of Management Studies 12 [2]: 170-179
  • Wysocka-Fijorek E., Lachowicz H. [2018]: Changes in prices, volume and value of wood raw material sold by the State Forests. Sylwan 162: 12-21
  • Zając S. [2001]: Lasy i leśnictwo w krajach Europy Środkowej i Wschodniej-proces transformacji i wyzwania. Międzynarodowe Warsztaty w Debem, 12-14 września 2001 r. Prace Instytutu Badawczego Leśnictwa, A: 71-77
  • Zeliaś A. [1997]: Teoria prognozy (Forecast theory), wyd. 3, PWE, Warszawa. PWE, Warszawa
  • Zubkowicz R. [2013]: Danina drogowa – pomysł na opodatkowanie LP (Road toll - an idea for taxing the State Forests). Las Polski: 18-19
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-596e4ab0-ff29-4212-aa8d-36ff92fc8529
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.