참고문헌
- D. Cole, The Global Uranium Market, Reserve Bank of Australia, 2015. Reserve Bank of Australia.
- D. Kryzia, L. Gawlik, Forecasting the price of uranium based on the costs of uranium deposits exploitation, Miner. Resour. Manag. 32 (2016) 93-110.
- N. Haneklaus, Y. Sun, B. Roland, B. Lottermoser, E. Schnug, To extract, or not to extract uranium from phosphate rock, that is the question, Environ. Sci. Technol. 51 (2) (2017) 753-754. URL 10.1021/acs.est.6b05506.
- B. Parker, Z. Zhang, L. Rao, J. Arnold, An overview and recent progress in the chemistry of uranium extraction from seawater, Dalton Trans. 3 (2018) 1-6. URL 10.1039/c7dt04058j.
- w. Mays, Limitations to progress in developing uranium resources, in: Proceedings of the 30th International Symposium on Uranium and Nuclear Energy, World Nuclear Association, 2005, pp. 39-60.
- I.A.E. Agency, Uram-2018: ebb and flow - the economics of uranium mining, accessed: 2019-06-20, https://www.iaea.org/newscenter/news/uram-2018-ebb-and-flow-the-economics-of-uranium-mining.
- International Monetary Fund, Primary commodity prices, accessed: 2019-06-20, https://www.imf.org/en/Research/commodity-prices.
- S. Kim, W. Ko, H. Nam, C. Kim, Y. Chung, S. Bang, Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost, Nucl. Eng. Technol. 49 (5) (2017) 1063-1070. https://doi.org/10.1016/j.net.2017.05.007
- Q. Yan, S. Wang, B. Li, Forecasting uranium resource price prediction by extreme learning machine with empirical mode decomposition and phase space reconstruction, Discrete Dynam Nat. Soc. (2014) 1-10, 2014.
- J. Chen1, Y. Zhao, Q. Song, Z. Zhou1, S. Yang, Exploration and mining evaluation wywtem and price prediction of uranium resources, Min. Miner. Depos. 12 (2018) 85-94.
- J. Chen, Y. Zhao, Q. Song, Z. Zhou, S. Yang, Exploration and mining evaluation system and price prediction of uranium resources, Min. Miner. Depos. 12 (2018) 85-94.
- S. Kahouli, Re-examining uranium supply and demand: new insights, Energy Policy 39 (2011) 358-376. https://doi.org/10.1016/j.enpol.2010.10.007
- M. Dittmar, The end of cheap uranium, Sci. Total Environ. 461-462 (2013) 792-798. https://doi.org/10.1016/j.scitotenv.2013.04.035
- A. Monnet, S. Gabriel, J. Percebois, Long-term availability of global uranium resources, Resour. Policy 53 (2017) 394-407. https://doi.org/10.1016/j.resourpol.2017.07.008
- V. Gomez, A. Maravall, Automatic modeling methods for univariate series, in: A Course in Time Series, John Wiley & Sons, Inc., 2001, pp. 171-201.
- R. Hyndman, A.B. Koehler, J.K. Ord, R.D. Snyder, Forecasting with Exponential Smoothing: the State Space Approach, Springer Science & Business Media, 2008.
- S. Makridakis, M. Hibon, The m3-competition: results, conclusions and implications, Int. J. Forecast. 16 (4) (2000) 451-476. https://doi.org/10.1016/S0169-2070(00)00057-1
- M.A. Villegas, D.J. Pedregal, Automatic selection of unobserved components models for supply chain forecasting, Int. J. Forecast. 35 (1) (2019) 157-169 (special Section: Supply Chain Forecasting). https://doi.org/10.1016/j.ijforecast.2017.11.001
- K.S. Chan, H. Tong, On estimating thresholds in autoregressive models, J. Time Ser. Anal. 7 (3) (1986) 179-190. https://doi.org/10.1111/j.1467-9892.1986.tb00501.x
- D. van Dijk, T. Terasvirta, P.H. Franses, Smooth transition autoregressive € models - a survey of recent developments, Econom. Rev. 21 (1) (2002) 1-47. https://doi.org/10.1081/ETC-120008723
- G. Sbrana, A. Silvestrini, Random switching exponential smoothing: a new estimation approach, Int. J. Prod. Econ. 211 (2019) 211-220. https://doi.org/10.1016/j.ijpe.2019.01.038
- T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Publishing, New York, 2009.
- S. Haykin, Neural Networks and Learning Machines, Prentice Hall, New Jersey, 2008.
- G. Zhang, Times series forecasting using a hybrid arima and neural network model, Neurocomputing 50 (2003) 159-175. https://doi.org/10.1016/S0925-2312(01)00702-0
- H. Zheng, J. Yuan, L. Chen, Short-term load forecasting using emd-lstm neural networks with a xgboost algorithm for feature importance evaluation, Energies 10 (2017) 1-20. https://doi.org/10.3390/en10010001
- S. Makridakis, E. Spiliotis, V. Assimakopoulos, Statistical and machine learning forecasting methods: concerns and ways forward, PLoS One 13 (3) (2018) 1-26, https://doi.org/10.1371/journal.pone.0194889. URL 10.1371/journal.pone.0194889.
- S. Makridakis, E. Spiliotis, V. Assimakopoulos, The m4 competition: results, findings, conclusion and way forward, Int. J. Forecast. 34 (2018) 802-808. https://doi.org/10.1016/j.ijforecast.2018.06.001
- G.E.P. Box, G.M. Jenkins, G.C. Reinsel, G.M. Ljung, Time Series Analysis: Forecasting and Control, fifth ed., John Wiley & Sons, 2015.
- V. Gomez, A. Maravall, Automatic modeling methods for univariate series, in: A Course in Time Series, John Wiley & Sons, Inc., 2001, pp. 171-201.
- R.J. Hyndman, Y. Khandakar, Automatic time series forecasting: the forecast package for R, J. Stat. Softw. 3 (27) (2008) 1-22.
- R.G. Brown, Statistical Forecasting for Inventory Control, McGraw-Hill, 1959.
- A.C. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge university press, 1989.
- P.C. Young, D.J. Pedregal, W. Tych, Dynamic harmonic regression, J. Forecast. 18 (6) (1999) 369-394. https://doi.org/10.1002/(SICI)1099-131X(199911)18:6<369::AID-FOR748>3.0.CO;2-K
- C.J. Taylor, D.J. Pedregal, P.C. Young, W. Tych, Environmental time series analysis and forecasting with the captain toolbox, Environ. Model. Softw 22 (6) (2007) 797-814. https://doi.org/10.1016/j.envsoft.2006.03.002
- J. Durbin, S.J. Koopman, Time Series Analysis by State Space Methods, second ed., Oxford University Press, 2012.
- V. Assimakopoulos, K. Nikolopoulos, The theta model: a decomposition approach to forecasting, Int. J. Forecast. 16 (4) (2000) 521-530. https://doi.org/10.1016/S0169-2070(00)00066-2
- N. Kourentzes, D. Barrow, S. Crone, Neural network ensemble operators for time series forecasting, Expert Syst. Appl. 41 (9) (2014) 4235-4244. https://doi.org/10.1016/j.eswa.2013.12.011
- N. Kourentzes, Nnfor: time series forecasting with neural networks, r package version 0.9.6, URL, https://CRAN.R-project.org/package=nnfor, 2019.
- D.K. Barrow, N. Kourentzes, Distributions of forecasting errors of forecast combinations: implications for inventory management, Int. J. Prod. Econ. 177 (2016) 24-33. https://doi.org/10.1016/j.ijpe.2016.03.017
- R. Hyndman, A. Koehler, Another look at measures of forecast accuracy, Int. J. Forecast. 22 (4) (2006) 679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001
- G.E.P. Box, D.R. Cox, An analysis of transformations, J. R. Stat. Soc. Ser. B 26 (2) (1964) 211-252.
- V.M. Guerrero, Time series analysis supported by power transformations, J. Forecast. 12 (1) (1993) 37-48. https://doi.org/10.1002/for.3980120104
- D. Eddelbuettel, C. Sanderson, Rcpparmadillo: accelerating r with high-performance c++ linear algebra, Comput. Stat. Data Anal. 71 (2014) 1054-1063, https://doi.org/10.1016/j.csda.2013.02.005. URL.
피인용 문헌
- Stationarity in the Prices of Energy Commodities. A Nonparametric Approach vol.14, pp.11, 2020, https://doi.org/10.3390/en14113324