This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Abulhanova, G. A., Chumarina, G. R., Nikiforova, E. G., & Sharifullina, T. A. (2016). Economic forecasting and personnel management of small and medium enterprises. Academy of Strategic Management Journal 15(4), 67-75.AbulhanovaG. A.ChumarinaG. R.NikiforovaE. G.&SharifullinaT. A.2016Economic forecasting and personnel management of small and medium enterprises1546775Search in Google Scholar
Aizenberg, I., Sheremetov, L., Villa-Vargas, L., & Martinez-Muñoz, J. (2016). Multilayer neural network with multi-valued neurons in time series forecasting of oil production. Neurocomputing 175, 980-989.AizenbergI.SheremetovL.Villa-VargasL.&Martinez-MuñozJ.2016Multilayer neural network with multi-valued neurons in time series forecasting of oil production175980–98910.1007/978-3-319-07491-7_7Search in Google Scholar
Alam, W., Sinha, K., Kumar, R. R., Ray, M., Rathod, S., Singh, K. N., & Arya, P. (2018). Hybrid linear time series approach for long term forecasting of crop yield. Indian Journal of Agricultural Sciences 88(8), 1275-1279.AlamW.SinhaK.KumarR. R.RayM.RathodS.SinghK. N.&AryaP.2018Hybrid linear time series approach for long term forecasting of crop yield8881275127910.56093/ijas.v88i8.82573Search in Google Scholar
Alva, I., Rojas, & J., Raymundo, C. (2020). Improving processes through the use of the 5S methodology and menu engineering to reduce production costs of a MSE in the hospitality sector in the department of Ancash. Advances in Intelligent Systems and Computing 1018, 818-824.AlvaI.&RaymundoC.2020Improving processes through the use of the 5S methodology and menu engineering to reduce production costs of a MSE in the hospitality sector in the department of Ancash1018818–82410.1007/978-3-030-25629-6_128Search in Google Scholar
Artun, E., Vanderhaeghen, & M., Murray, P. (2016). A pattern-based approach to waterflood performance prediction using knowledge management tools and classical reservoir engineering forecasting methods. Gas and Coal Technology, 13(1) 19-40.ArtunE.&MurrayP.2016A pattern-based approach to waterflood performance prediction using knowledge management tools and classical reservoir engineering forecasting methods13119–4010.2118/169587-MSSearch in Google Scholar
Barinova, O. I., & Shikhova, O. A. (2016). Methodological problems of milk cost forecasting in operational cost management. Innovative Way of Development of Agro-Industrial Complex: Collection of Scientific Works on Materials of XXXIX International Scientific-Practical Conference of the Faculty (pp. 156-161).BarinovaO. I.&ShikhovaO. A.2016Methodological problems of milk cost forecasting in operational cost management156161Search in Google Scholar
Box, G., & Jenkins, G. (1970). Time series Analysis: Forecasting and Control San Francisco, United States: Holden-Day.BoxG.&JenkinsG.1970San Francisco, United StatesHolden-DaySearch in Google Scholar
Chen, X. J., Tang, Z.-H., & Li, J. F. (2012). Preliminary study on BIPV grid-connected generation system production forecasting. Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control 40(18), 81-85.ChenX. J.TangZ.-H.&LiJ. F.2012Preliminary study on BIPV grid-connected generation system production forecasting40188185Search in Google Scholar
Chunyan, L., & Jun, C. (2009). Traffic Accident Macro Forecast Based on ARIMAX Model. International Conference on Measuring Technology and Mechatronics Automation, 3, 633-636.ChunyanL.&JunC.2009Traffic Accident Macro Forecast Based on ARIMAX Model3633–636Search in Google Scholar
Cieślak, M. (Ed.). (2005). Prognozowanie gospodarcze. Metody i zastosowania [Economic forecasting. Methods and applications]. Warszawa, Poland: Wydawnic-two Naukowe PWN.CieślakM.2005Prognozowanie gospodarcze. Metody i zastosowaniaWarszawa, PolandWydawnic-two Naukowe PWNSearch in Google Scholar
Clark, A. J., Lake, L. W., & Patzek, T. W. (2011). Production forecasting with logistic growth models. Proceedings - SPE Annual Technical Conference and Exhibition 1, 184-194.ClarkA. J.LakeL. W.&PatzekT. W.2011Production forecasting with logistic growth models1184–19410.2118/144790-MSSearch in Google Scholar
Cortez, P., Rocha, M., Machado, J., & Neves, J. (1995). A neural network-based time series forecasting system. Proceedings of IEEE International Conference on Neural NetworksCortezP.RochaM.MachadoJ.&NevesJ.1995A neural network-based time series forecasting system10.1109/ICNN.1995.487836Search in Google Scholar
Czerwiński, Z. (1992). Dylematy ekonomiczne [Economic dilemmas]. Warszawa, Poland: Państwowe Wydawnictwo Ekonomiczne.CzerwińskiZ.1992Dylematy ekonomiczneWarszawa, PolandPaństwowe Wydawnictwo EkonomiczneSearch in Google Scholar
de Oliveira, R. C., Mendes-Moreira, J., & Ferreira, C. A. (2018). Agribusiness intelligence: Grape production forecast using data mining techniques. Advances in Intelligent Systems and Computing 747, 3-8.de OliveiraR. C.Mendes-MoreiraJ.&FerreiraC. A.2018Agribusiness intelligence: Grape production forecast using data mining techniques7473–810.1007/978-3-319-77700-9_1Search in Google Scholar
Dupré, A., Drobinski, P. A., Alonzo, B. A., Badosa, J. A., Briard, C. C., & Plougonven, R. (2020). Sub-hourly forecasting of wind speed and wind Energy. Renewable Energy 145, 2373-2379.DupréA.DrobinskiP. A.AlonzoB. A.BadosaJ. A.BriardC. C.&PlougonvenR.2020Sub-hourly forecasting of wind speed and wind Energy1452373–237910.1016/j.renene.2019.07.161Search in Google Scholar
Dzikevičius, A., & Šaranda, S. (2011). Smoothing techniques for market fluctuation signals. Business: Theory and Practice 12(1), 63-74.DzikevičiusA.&ŠarandaS.2011Smoothing techniques for market fluctuation signals121637410.3846/btp.2011.07Search in Google Scholar
Ejdys, J., Halicka, K., & Godlewska, J. (2015). Prognozowanie cen energii elektrycznej na giełdzie energii [Forecasting electricity prices on the energy exchange]. Zeszyty Naukowe. Organizacja i Zarządzanie. Politechnika Śląska, 77 53-61.EjdysJ.HalickaK.&GodlewskaJ.2015Prognozowanie cen energii elektrycznej na giełdzie energii [Forecasting electricity prices on the energy exchange]7753–61Search in Google Scholar
Ejdys, J., Halicka, K., & Winkowski, C. (2014). Predicting oil prices. Journal of Machine Construction and Maintenance 92(1), 5-13.EjdysJ.HalickaK.&WinkowskiC.2014Predicting oil prices921513Search in Google Scholar
Elgharbi, S., Esghir, M., Ibrihich, O., Abarda, A., El Hajji, S., & Elbernoussi, S. (2020). Grey-Markov Model for the Prediction of the Electricity Production and Consumption. Lecture Notes in Networks and Systems 81, 206-219.ElgharbiS.EsghirM.IbrihichO.AbardaA.El HajjiS.&ElbernoussiS.2020Grey-Markov Model for the Prediction of the Electricity Production and Consumption81206–21910.1007/978-3-030-23672-4_16Search in Google Scholar
Eraslan, E. (2009). The estimation of product standard time by artificial neural networks in the molding industry. Mathematical Problems in Engineering2009, 1-12.EraslanE.2009The estimation of product standard time by artificial neural networks in the molding industry20091–1210.1155/2009/527452Search in Google Scholar
Eraslan, E., Farhan, A., Hassnain, S., Irum R., & Abdul, S. (2011). Forecasting milk production in Pakistan. Pakistan Journal of Agricultural Research 24(1-4), 82-85.EraslanE.FarhanA.HassnainS.IrumR.&AbdulS.2011Forecasting milk production in Pakistan241-48285Search in Google Scholar
Gligor, A., Dumitru, C.-D., & Grif, H.-S. (2018). Artificial intelligence solution for managing a photovoltaic energy production unit. Procedia Manufacturing 22, 626-633.GligorA.DumitruC.-D.&GrifH.-S.2018Artificial intelligence solution for managing a photovoltaic energy production unit22626–63310.1016/j.promfg.2018.03.091Search in Google Scholar
Guanwu, J., Minzhou, L., Keqiang, B., & Saixuan, C. (2017). A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm. Applied Sciences 7(10), 1-13.GuanwuJ.MinzhouL.KeqiangB.&SaixuanC.2017A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm71011310.3390/app7100969Search in Google Scholar
Gudanowska, A. E. (2017). A map of current research trends within technology management in the light of selected literature. Management and Production Engineering Review 8(1), 78-88.GudanowskaA. E.2017A map of current research trends within technology management in the light of selected literature81788810.1515/mper-2017-0009Search in Google Scholar
Gyulai, D., Pfeiffer, A., Nick, G., Gallina, V., Sihn, W., & Monostori, L. (2018). Lead time prediction in a flow-shop environment with analytical and machine learning approaches. IFAC-PapersOnLine 51(11), 1029-1034.GyulaiD.PfeifferA.NickG.GallinaV.SihnW.&MonostoriL.2018Lead time prediction in a flow-shop environment with analytical and machine learning approaches51111029103410.1016/j.ifacol.2018.08.472Search in Google Scholar
Halicka, K. (2016). Prospektywna analiza technologii – metodologia i procedury badawcze [Prospective technology analysis – research methodology and procedures]. Białystok, Poland: Oficyna Wydawnicza Politechniki Białostockiej.HalickaK.2016Prospektywna analiza technologii – metodologia i procedury badawczeBiałystok, PolandOficyna Wydawnicza Politechniki BiałostockiejSearch in Google Scholar
Halicka, K. (2017). Main concepts of technology analysis in the light of the literature on the subject. Procedia Engineering 182, 291-298.HalickaK.2017Main concepts of technology analysis in the light of the literature on the subject182291–29810.1016/j.proeng.2017.03.196Search in Google Scholar
Jae, R., Shim, J. K., & Siegel, J. G. (2009). Modern Cost Management and Analysis Barron’s Educational Series.JaeR.ShimJ. K.&SiegelJ. G.2009Barron’s Educational SeriesSearch in Google Scholar
Jain, A., Patel, N., Hammonds, P., & Pandey, S. (2018). A smart software system for flow assurance management Society of Petroleum Engineers SPE Asia Pacific Oil and Gas Conference and Exhibition.JainA.PatelN.HammondsP.&PandeyS.2018SPE Asia Pacific Oil and Gas Conference and Exhibition10.2118/191951-MSSearch in Google Scholar
Jones, D. (2004). Estimation of power system parameters. IEEE Transactions on Power Systems 19(4), 1980-1989.JonesD.2004Estimation of power system parameters1941980198910.1109/TPWRS.2004.835671Search in Google Scholar
Kamiński, A. (1974). Metoda, technika, procedura badawcza w pedagogice empirycznej [Method, technique, research procedure in empirical pedagogy]. In R. Wroczyński, & T. Pilch (Ed.), Metodologia pedagogiki społecznej [Methodology of social pedagogy]. Wrocław, Poland: Wydawnictwo PAN.KamińskiA.1974Metoda, technika, procedura badawcza w pedagogice empirycznej [Method, technique, research procedure in empirical pedagogy]WroczyńskiR.&PilchT.Wrocław, PolandWydawnictwo PANSearch in Google Scholar
Kikolski, M., & Ko, C. H. (2018). Facility layout design – review of current research directions. Engineering Management in Production and Services, 10(3), 70-79.KikolskiM.&KoC. H.2018Facility layout design – review of current research directions103707910.2478/emj-2018-0018Search in Google Scholar
Korol, T. (2010). Systemy ostrzegania przedsiębiorstw przed ryzykiem upadłości [Systems warning companies about the risk of bankruptcy]. Warszawa, Poland: Oficyna Ekonomiczna Grupa Wolters Kluwer.KorolT.2010Systemy ostrzegania przedsiębiorstw przed ryzykiem upadłościWarszawa, PolandOficyna Ekonomiczna Grupa Wolters KluwerSearch in Google Scholar
Kot, S., & Grondys, K. (2011). Theory of inventory management based on demand forecasting. Polish Journal of Management Studies, 3(1), 147-155.KotS.&GrondysK.2011Theory of inventory management based on demand forecasting31147155Search in Google Scholar
Kuladzhi, T., Babkin, I., Murtazayev, S.-A., & Golovina, T. (2017). Digital matrix micro forecast of informational and telecommunicational products cost value Proceedings of the 2017 International Conference “Quality Management, Transport and Information Security, Information Technologies”.KuladzhiT.BabkinI.MurtazayevS.-A.&GolovinaT.2017Proceedings of the 2017 International Conference “Quality Management, Transport and Information Security, Information Technologies”10.1109/ITMQIS.2017.8085802Search in Google Scholar
Kyzenko, O., Hrebeshkova, O., & Grebeshkov, O. (2017). Business intelligence in the economic management of organization Forum Scientiae Oeconomia, 5(2), 15-27.KyzenkoO.HrebeshkovaO.&GrebeshkovO.2017Business intelligence in the economic management of organization521527Search in Google Scholar
Lai, X., Shui, H., & Ni, J. (2018). A two-layer long short-Term memory network for bottleneck prediction in multi-job manufacturing systems ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC.LaiX.ShuiH.&NiJ.2018ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC10.1115/MSEC2018-6678Search in Google Scholar
Laick, S. (2012). Using Delphi methodology in information system research. International Journal of Management Cases 14(4), 261-268.LaickS.2012Using Delphi methodology in information system research14426126810.5848/APBJ.2012.00103Search in Google Scholar
Li, S., Ma, X., & Yang, C. (2018). A novel structure-adaptive intelligent grey forecasting model with full-order time power terms and its application. Computers and Industrial Engineering 120, 53-67.LiS.MaX.&YangC.2018A novel structure-adaptive intelligent grey forecasting model with full-order time power terms and its application12053–6710.1016/j.cie.2018.04.016Search in Google Scholar
Lin, B., Wong, S. F., & Ho, W. I. (2015). Study on the production forecasting based on grey neural network model in automotive industry IEEE International Conference on Industrial Engineering and Engineering Management.LinB.WongS. F.&HoW. I.2015IEEE International Conference on Industrial Engineering and Engineering Management10.1109/IEEM.2014.7058659Search in Google Scholar
Linstone, H. A., & Turoff, M. (1975). The Delphi method: techniques and applications Addison-Wesley Pub. Co.LinstoneH. A.&TuroffM.1975Addison-Wesley Pub. CoSearch in Google Scholar
Maciąg, A., Pietroń, R., & Kukla, S. (2013). Prognozowanie i symulacja w przedsiębiorstwie [Business forecasting and simulation]. Warszawa, Poland: Polskie Wydawnictwo Ekonomiczne.MaciągA.PietrońR.&KuklaS.2013Prognozowanie i symulacja w przedsiębiorstwieWarszawa, PolandPolskie Wydawnictwo EkonomiczneSearch in Google Scholar
Meling, L. M., Morkeseth, P. O., & Langeland, T. (1988). Production forecasting for gas fields with multiple reservoirs of limited extent Society of Petroleum Engineers of AIME, (Paper) SPE SIGMA.MelingL. M.MorkesethP. O.&LangelandT.1988Society of Petroleum Engineers of AIME, (Paper) SPE SIGMASearch in Google Scholar
Merchant, M. (1970). Technological forecasting and production engineering research. Ann CIRP 18(1), 5-11.MerchantM.1970Technological forecasting and production engineering research181511Search in Google Scholar
Mustafa, I. K., & Jbara, O. K. (2018). Forecasting the food gap and production of wheat crop in Iraq for the period (2016-2025). Iraqi Journal of Agricultural Sciences 49(4), 560-568.MustafaI. K.&JbaraO. K.2018Forecasting the food gap and production of wheat crop in Iraq for the period (2016-2025)49456056810.36103/ijas.v49i4.63Search in Google Scholar
Mustafaeva, U. Z. (2007). Regression analysis of the dependence of the volume of production on the cost of it. Econ Agric Process Enterprises, 5, 46-47.MustafaevaU. Z.2007Regression analysis of the dependence of the volume of production on the cost of it546–47Search in Google Scholar
Nazarko, J. (Ed.). (2004). Prognozowanie w zarządzaniu przedsiębiorstwem, cz. 2. Prognozowanie na podstawie szeregów czasowych [Forecasting in business management, part 2. Forecasting based on time series]. Białystok, Poland: Wydawnictwo Politechniki Białostockiej.NazarkoJ2004Prognozowanie w zarządzaniu przedsiębiorstwem, cz. 2. Prognozowanie na podstawie szeregów czasowychBiałystok, PolandWydawnictwo Politechniki BiałostockiejSearch in Google Scholar
Ngadono, T. S., & Ikatrinasari, Z. F. (2018). Forecasting of PVB Film Using ARIMA. IOP Conference Series: Materials Science and Engineering, 453(1).NgadonoT. S.&IkatrinasariZ. F.2018Forecasting of PVB Film Using ARIMA453110.1088/1757-899X/453/1/012012Search in Google Scholar
Okubo, H., Weng, J., Kaneko, R., Simizu, T., & Onari, H. (2000). Production lead-time estimation system based on neural network Proceedings of Asia-Pacific Region of Decision Sciences Institute.OkuboH.WengJ.KanekoR.SimizuT.&OnariH.2000Proceedings of Asia-Pacific Region of Decision Sciences InstituteSearch in Google Scholar
Onaran, E., & Yanık, S. (2020). Predicting cycle times in textile manufacturing using artificial neural network. Advances in Intelligent Systems and Computing, 1029, 305-312.OnaranE.&YanıkS.2020Predicting cycle times in textile manufacturing using artificial neural network1029305–31210.1007/978-3-030-23756-1_38Search in Google Scholar
Qader, S. H., Dash, J., & Atkinson, P. M. (2018). Forecasting wheat and barley crop production in arid and semiarid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq. Science of the Total Environment, 613-614, 250-262.QaderS. H.DashJ.&AtkinsonP. M.2018Forecasting wheat and barley crop production in arid and semiarid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq613-614250–26210.1016/j.scitotenv.2017.09.05728915461Search in Google Scholar
Radziszewski, P., Nazarko, J., Vilutiene, T., Dębkowska, K., Ejdys, J., Gudanowska, A., Halicka, K., Kilon, J., Kononiuk, A., Kowalski, K. J., Król, J. B., Nazarko, Ł., & Sarnowski, M. (2016). Future Trends in Road Technologies Development in the Context of Environmental Protection. Baltic Journal of Road and Bridge Engineering, 11(2), 160-168.RadziszewskiP.NazarkoJ.VilutieneT.DębkowskaK.EjdysJ.GudanowskaA.HalickaK.KilonJ.KononiukA.KowalskiK. J.KrólJ. B.NazarkoŁ.&SarnowskiM.2016Future Trends in Road Technologies Development in the Context of Environmental Protection11216016810.3846/bjrbe.2016.19Search in Google Scholar
Rahmat, R. F., Nurmawan, Sembiring, S., Syahputra, M.F., & Fadli (2018). Adaptive neuro-fuzzy inference system for forecasting rubber milk production. IOP Conference Series: Materials Science and Engineering, 308(1), 012014.RahmatR. F.NurmawanSembiring, S.SyahputraM.F.&2018Adaptive neuro-fuzzy inference system for forecasting rubber milk production308101201410.1088/1757-899X/308/1/012014Search in Google Scholar
Sagheer, A., & Kotb, M. (2018). Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323, 203-213.SagheerA.&KotbM.2018Time series forecasting of petroleum production using deep LSTM recurrent networks323203–21310.1016/j.neucom.2018.09.082Search in Google Scholar
Sarma, P., Lawrence, K., Zhao, Y., Kyriacou, S., & Saks, D. (2018). Implementation and assessment of production optimization in a steamflood using machine-learning assisted modeling Society of Petroleum Engineers - SPE International Heavy Oil Conference and Exhibition, HOCE 2018.SarmaP.LawrenceK.ZhaoY.KyriacouS.&SaksD.2018Society of Petroleum Engineers - SPE International Heavy Oil Conference and Exhibition, HOCE 201810.2118/193680-MSSearch in Google Scholar
Siderska, J., & Jadaa K. S. (2018). Cloud manufacturing: a service-oriented manufacturing paradigm. A review paper Engineering Management in Production and Services 10(1), 22-31.SiderskaJ.&JadaaK. S.2018Cloud manufacturing: a service-oriented manufacturing paradigm101223110.1515/emj-2018-0002Search in Google Scholar
Skulmoski, G. J., Hartman, F. T., & Krahn, J. (2007). The Delphi Method for Graduate Research. Journal of Information Technology Education 6, 1-21.SkulmoskiG. J.HartmanF. T.&KrahnJ.2007The Delphi Method for Graduate Research61–2110.28945/199Search in Google Scholar
Słownik nowy języka polskiego [New polish language dictionary]. (2002). Warszawa, Poland: Wydawnictwo Naukowe PWN.2002Warszawa, PolandWydawnictwo Naukowe PWNSearch in Google Scholar
Sobczyk, M. (2008). Prognozowanie. Teoria, Przykłady, Zadania [Forecasting. Theory, Examples, Tasks]. Warszawa, Poland: Wydawnictwo Placet.SobczykM.2008Prognozowanie. Teoria, Przykłady, ZadaniaWarszawa, PolandWydawnictwo PlacetSearch in Google Scholar
Spicer, J. H. (1970). Cybernetic approach to strategic planning, marketing and production control. Rail International, 1(6), 400-404.SpicerJ. H.1970Cybernetic approach to strategic planning, marketing and production control16400404Search in Google Scholar
Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P., & Bokrantz, J. (2018). A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines. Computers and Industrial Engineering 125, 533-544.SubramaniyanM.SkooghA.SalomonssonH.BangaloreP.&BokrantzJ.2018A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines125533–54410.1016/j.cie.2018.04.024Search in Google Scholar
Susanto, S., Tanaya, P. I., & Soembagijo, A. S. (2012). Formulating standard product lead time at a textile factory using artificial neural networks Proceeding of 2012 International Conference on Uncertainty Reasoning and Knowledge Engineering, URKE 2012, 6319595, 99-104.SusantoS.TanayaP. I.&SoembagijoA. S.2012Formulating standard product lead time at a textile factory using artificial neural networks2012631959599–10410.1109/URKE.2012.6319595Search in Google Scholar
Szpilko, D. (2017). Tourism Supply Chain – overview of selected literature. Procedia Engineering 182, 687-693.SzpilkoD.2017Tourism Supply Chain – overview of selected literature182687–69310.1016/j.proeng.2017.03.180Search in Google Scholar
Tariq, Z. (2018). An automated flowing bottom-hole pressure prediction for a vertical well having multiphase flow using computational intelligence techniques Society of Petroleum Engineers - SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition 2018, SATS 2018.TariqZ.2018Society of Petroleum Engineers - SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition 2018, SATS 201810.2118/192184-MSSearch in Google Scholar
Tariq, Z., Mahmoud, M., & Abdulraheem, A. (2019). Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques. Journal of Petroleum Exploration and Production TechnologyTariqZ.MahmoudM.&AbdulraheemA.2019Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques10.1007/s13202-019-0728-4Search in Google Scholar
Theocharides, S., Makrides, G., Georghiou, G. E., & Kyprianou, A. (2018). Machine learning algorithms for photovoltaic system power output prediction. 2018 IEEE International Energy Conference, Energycon, 2018, 1-6.TheocharidesS.MakridesG.GeorghiouG. E.&KyprianouA.2018Machine learning algorithms for photovoltaic system power output prediction20181–610.1109/ENERGYCON.2018.8398737Search in Google Scholar
Tkachev, S. I., Voloshchuk, L. A., Melnikova, Y. V., Pakhomova, T. V., & Rubtsova, S. N. (2018). Economic and mathematical modeling of quantitative assessment of financial risks of agricultural enterprises. Journal of Applied Economic Sciences 13(3), 823-829.TkachevS. I.VoloshchukL. A.MelnikovaY. V.PakhomovaT. V.&RubtsovaS. N.2018Economic and mathematical modeling of quantitative assessment of financial risks of agricultural enterprises133823829Search in Google Scholar
Trubaev, P. A., & Tarasyuk, P. N. (2017). Evaluation of energy-saving projects for generation of heat and heat supply by prime cost forecasting method. International Journal of Energy Economics and Policy 7(5), 201-208.TrubaevP. A.&TarasyukP. N.2017Evaluation of energy-saving projects for generation of heat and heat supply by prime cost forecasting method75201208Search in Google Scholar
Wang, A., & Li, S. (2011). Prediction on the developing trend of global electric automobile based on the logistic model BMEI 2011 - Proceedings 2011 International Conference on Business Management and Electronic Information.WangA.&LiS.2011BMEI 2011 - Proceedings 2011 International Conference on Business Management and Electronic InformationSearch in Google Scholar
Wang, C., & Jiang, P. (2019). Deep neural networks based order completion time prediction by using real-time job shop RFID data. Journal of Intelligent Manufacturing, 30(3) 1303-1318.WangC.&JiangP.2019Deep neural networks based order completion time prediction by using real-time job shop RFID data3031303131810.1007/s10845-017-1325-3Search in Google Scholar
Wasilewski, J. (2014). Application of ARIMAX models to short-term electric energy production forecasting at wind micro power plants. Przegląd Elektrotechniczny 90(7), 135-138.WasilewskiJ.2014Application of ARIMAX models to short-term electric energy production forecasting at wind micro power plants907135138Search in Google Scholar
Wickens, L. M., & De Jonge, G. (2006). Increasing confidence in production forecasting through risk-based integrated asset modelling, captain field case study. Society of Petroleum Engineers, 68th European Association of Geoscientists and Engineers Conference and Exhibition, incorporating SPE EUROPEC 2006, EAGE 2006: Opportunities in Mature Areas 6, 3162-3174.WickensL. M.&De JongeG.2006Increasing confidence in production forecasting through risk-based integrated asset modelling, captain field case study63162–317410.2118/99937-MSSearch in Google Scholar
Winkowska, J., Szpilko, D., & Pejić, S. (2019). Smart city concept in the light of the literature review. Engineering Management in Production and Services 11(2), 70-86.WinkowskaJ.SzpilkoD.&PejićS.2019Smart city concept in the light of the literature review112708610.2478/emj-2019-0012Search in Google Scholar
Witek-Crabb, A. (2016). Maturity of strategic management in organizations. Oeconomia Copernicana 7(4), 669-682.Witek-CrabbA.2016Maturity of strategic management in organizations7466968210.12775/OeC.2016.037Search in Google Scholar
Wu, Y., Hou, F., & Cheng, X. (2017). Real-time prediction of styrene production volume based on machine learning algorithms Lecture Notes in Computer Science 10357 LNAI, 301-312.WuY.HouF.&ChengX.2017Lecture Notes in Computer Science 10357 LNAI301–31210.1007/978-3-319-62701-4_24Search in Google Scholar
Yang, L., Lin, H., Gong, Y., & Zhou, T. (2018). Coalbed methane production forecasting based on dynamic PSO neural network model ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery.YangL.LinH.GongY.&ZhouT.2018ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery10.1109/FSKD.2017.8393405Search in Google Scholar
Yureneva, T., Barinova, O., & Golubeva, S. (2020). Forecasting the prime cost of milk production in an uncertain environment. Smart Innovation, Systems and Technologies 138, 678-693.YurenevaT.BarinovaO.&GolubevaS.2020Forecasting the prime cost of milk production in an uncertain environment138678–69310.1007/978-3-030-15577-3_63Search in Google Scholar
Yureneva, T. G., & Barinova, O. I. (2016). Cost differentiation in the dairy industry for short-term forecasting of milk cost. Management Accounting, 4, 28-37.YurenevaT. G.&BarinovaO. I.2016Cost differentiation in the dairy industry for short-term forecasting of milk cost428–37Search in Google Scholar
Zeliaś, A. (1997). Teoria prognozy [Forecast theory]. Warszawa, Poland: Polskie Wydawnictwo Ekonomiczne.ZeliaśA.1997Warszawa, PolandPolskie Wydawnictwo EkonomiczneSearch in Google Scholar
Zeng, B. L., Chengming L. S., Liu, S., & Li, C. (2016). A novel multi-variable grey forecasting model and its application in forecasting the amount of motor vehicles in Beijing. Computers & Industrial Engineering, 101 479-489.ZengB. L.ChengmingL. S.LiuS.&LiC.2016A novel multi-variable grey forecasting model and its application in forecasting the amount of motor vehicles in Beijing101479–48910.1016/j.cie.2016.10.009Search in Google Scholar
Zhang, C., Orangi, A., Bakshi, A., Da Sie, W., & Prasanna, V. K. (2006). Model-based framework for oil production forecasting and optimization: A case study in integrated asset management. 2006 SPE Intelligent Energy Conference and Exhibition 2, 527-533.ZhangC.OrangiA.BakshiA.Da SieW.&PrasannaV. K.2006Model-based framework for oil production forecasting and optimization: A case study in integrated asset management2527–53310.2118/99979-MSSearch in Google Scholar
Zhao, H., Huang, F., Li, L., & Zhang, C. (2018). Optimization of wastewater anaerobic digestion treatment based on ga-bp neural network. Desalination and Water Treatment 122, 30-35.ZhaoH.HuangF.LiL.&ZhangC.2018Optimization of wastewater anaerobic digestion treatment based on ga-bp neural network12230–3510.5004/dwt.2018.22596Search in Google Scholar
Zhou, C. L., & Liu, M. (2009). Application research on oil production forecasting based on BP neural network. Wuhan Ligong Daxue Xuebao/Journal of Wuhan University of Technology 31(3), 125-129.ZhouC. L.&LiuM.2009Application research on oil production forecasting based on BP neural network313125129Search in Google Scholar