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Online Forecasting of the Solar Energy Production

forecasting and resources assessment. Elsevier, Oxford (2013). [5] J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F.J. Martinez-de-Pison, F. Antonanzas-Torres. Sol Energy 136 (2016) 78-111. [6] M. Paulescu, E. Paulescu, P. Gravila, V. Badescu. Springer, London (2013). [7] M. Paulescu, O. Mares, E. Paulescu, N. Stefu, A. Pacurar, D. Calinoiu, P. Gravila, N. Pop, R. Boata. Energy Convers Manage 79 (2014) 690-697. [8] Delta-T Devices. WS-GP2 Advanced Automatic Weather Station System [9] V. Badescu, M

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New system of employment forecasting in Poland

References Acemoglu D., Autor D. (2011), Skills, Tasks and Technologies: Implications for Employment and Earning [in] O. Ashenfelter, D. Card (eds), Handbook of Labour Economics , Vol. 4B, Elsevier, Amsterdam, pp. 1043–1171. Arendt Ł., Ulrichs M. (eds) (2012), Best practices in forecasting labour demand in Europe , Instytut Pracy i Spraw Socjalnych, Warsaw. Bukowski M. (ed.) (2010), Employment in Poland 2008. Work over the life course, Human Resources Development Centre, Warsaw. Capellen A., Gjefsen H., Gjelsvik M., Holm I., Stolen N.M. (2013

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Statistical Forecasting of the Indicators of Polish Airport’s Operations

References Civil Aviation Authority (2009). Analysis of air transport market in Poland 2004-2007 . Warszawa. Civil Aviation Authority (2011). Analysis of air transport market in Poland in 2010 . Warszawa. Diebold, F. & Mariano, R.S. (1995). Comparing Predictive Accuracy. Journal of Business & Economic Statistics , 13(3), 253-263. Hyndman, R. & Khandakar Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software , 27(3), 1

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Application of the Varma Model for Sales Forecast: Case of Urmia Gray Cement Factory

Protection and Capital Structure: International Evidence. Journal of Multinational Financial Management, 17(1), 30-44. Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analyses: Forecasting and Control. 2nd ed., Holden -Day, San Francisco. Brockwell, P. J., & Davis, R. A. (1987). Time Series: Theory and Methods. Springer, New York. Curtis, A., & Lundholm, R. J. (2013). Forecasting Sales: A Model and Some Evidence from the Retail Industry, Contemporary Accounting Research, 31(2), 581-608. Frees, E. W

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Forecasting Randomly Distributed Zero-Inflated Time Series

., Doszyń, M., Dmytrów, K. (2017). Comparison of the Effectiveness of Forecasts Obtained by Means of Selected Probability Functions with Respect to Forecast Error Distributions. Communications in Statistics. Simulation and Computation, 46 (5), 3667-3679. DOI: 10.1080/03610918.2015.1100734. Winkelmann, R. (2008). Econometric Analysis of Count Data. Berlin, Heidelberg: Springer-Verlag. Yang, M. (2012). Statistical models for count time series with excess zeros. University of Iowa.

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Machine Learning Platform for Profiling and Forecasting at Microgrid Level

R eferences [1] T. V. Ark, “Ask About AI - The Future of Work and Learning,” Getting Smart Staff, 2017. [2] E. Koblentz, “How to implement AI and machine learning,” TechRepublic, 2016. [3] Z. Mohamed and P. Bodger, “Forecasting electricity consumption in New Zealand using economic and demographic variables,” Energy, vol. 30, no. 10, pp. 1833–1843, 2005. [4] M. Yang and X. Yu, “China’s rural electricity market – a quantitative analysis,” Energy, vol. 29, no. 7, pp. 961–977, 2004. https

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Water Flow Forecasting and River Simulation for Flood Risk Analysis

References [1] P.-K. Chiang, P. Willems, J. Berlamont, "A conceptual river model to support real-time flood control," in Demer,’ River Flow 2010 - Dittrich, Koll, Aberle & Geisenhainer, 2010, pp. 1407-1414. [2] K.P. Georgakakos, R. Krzysztofowicz, "Probabilistic and Ensemble Forecasting (Editorial)," Journal of Hydrology, 249(1), 2001, pp.1-4. [3] C. Tucci, W. Collischonn, "Flood forecasting," WMO Bulletin 55 (3), 2006, pp. 179-184. [4] B. Pradhan, "Effective Flood Monitoring System Using

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Forecasting Passenger Traffic for a Regional Airport

5. References Airport Cooperative Research Program (ACRP), (2007). Airport Aviation Activity Forecasting. Antonio Danesi, Luca Mantecchini and Filippo Paganelli, (2017). Long-Term And Short-Term Forecasting Techniques For Regional Airport Planning, ARPN Journal of Engineering and Applied Sciences , VOL. 12, NO. 3, FEBRUARY 2017, ISSN 1819-6608. Bent Flyvbjerg, Mette K. Skamris Holm & Søren L. Buhl (2007). How (In)accurate Are Demand Forecasts in Public Works Projects?: The Case of Transportation, Journal of the American Planning Association

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Applying Markov Chains for NDVI Time Series Forecasting of Latvian Regions

Biennial Meeting, Leipzig, Germany, 2012. [3] E. Sahebjalal and K. Dashtekian, “Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods,” African J. of Agricultural Research , vol. 8, no. 37, pp. 4614-4622, September 26, 2013. [4] M. Manobavan, N. S. Lucas, D. S. Boyd and N. Petford, “Forecasting the interannual trends in terrestrial vegetation dynamics using time series modelling techniques,” presented at the ForestSAT Symposium Heriot Watt University , Edinburgh, United Kingdom

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Fuzzy Supervised Multi-Period Time Series Forecasting

References 1. Chang, J. R., L. Y. Wei, C. H. Cheng. A Hybrid ANFIS Model Based on AR and Volatility for TAIEX Forecasting. – Applied Soft Computing Journal, Vol. 11 , 2011, Issue 1, pp. 1388-1395. 2. Chen, S. M. Forecasting Enrollments Based on Fuzzy Time Series. – Fuzzy Sets and Systems, Vol. 81 , 1996, Issue 3, pp. 311-319. 3. Chen, S. M., W. S. Jian. Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups, Similarity Measures and PSO Techniques. – Information Sciences, Vol. 391-392 , 2017, pp. 65

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