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Bibliography Akaike H., 1974, A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19(6), pp. 716-723. Anvari S., Tuna S., Canci M., Turkay M., 2016, Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems , Journal of Advanced Transportation, (50), pp. 25-49. Armstrong J.S., 1985, Long-Range Forecasting: From Crystal Ball to Computer , 2nd. ed., Wiley. Assimakopoulos V., Makridakis S., Spiliotis E., 2018, Statistical and Machine Learning forecasting methods: Concerns and

Abstract

The number of future sales of residential real estate in the primary developer depends mainly on the factor of demand, consumer needs for housing in established market conditions. One way to determine the future market capacity for the construction of residential demand forecasts.

The company is in a very favorable position, having a reliable forecast of future demand. Forecast has an impact on the ability to achieve the intended purpose of building and as a result allows you to minimize the risk of return on invested capital at the same time ensuring a profit.

Placed forecast future sales of apartments in the time horizon of two years in advance, taking into account a series of standardized construction, contains processed numerical data on a quarterly sale by X developer in the years from 2008 to 2012.

Erected on the basis of forecast model and found very useful model to predict seasonal fluctuations in demand numerical sale in the test development company.

References [1] Heinemann D., Lorenz E., Girodo M. (2006). Forecasting of solar radiation in: Dunlop, E.D., Wald, L., Suri, M. (Eds.), Solar Energy Resource Management for Electricity Generation from Local Level to Global Scale . Nova Science Publishers, Hauppauge. [2] Mellit A., Pavan A.M. (2010). Sol. Energy 84 (5), (pp. 807-821) [3] IEA, (2007). Energy Technologies at the Cutting Edge , International Energy Agency, OECD Publication Service, OECD, Paris. [4] Grell G., Dudhia J., Stauffer D. (1998). A Description of the Fifth-Generation Penn State

References Abrahart, R.J., See, L.M., Dawson, C.W., Shamseldin, A.Y., Wilby, R.L., 2010. Nearly Two Decades of Neural Network Hydrological Modeling. In: Sivakumar, B., Berndtsson, R., (Eds.): Advances in data-based approaches for hydrologic modeling and forecasting. World Scientific. Arduino, G., Reggiani, P., Todini, E., 2005. Recent advances in flood forecasting and flood risk assessment. Hydrology and Earth System Sciences, 9, 4, 280-284. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, 2000a. Artificial neural networks in

may voluntarily disclose information on its earnings forecasts. Publication of such forward-looking information is aimed at limiting the uncertainty of stock investors interested in the purchase of the company’s shares in IPO [ Jog and McConomy 2003 ; Chong and Ho 2007 , p. 63; Bédard et al., 2016 , p. 236]. Thus, the forecasts of financial results become a signal sent to the public about the future value of the company [ Verrecchia, 1983 ]. In turn, Cohen and Dean [2005] suggest that one such observable means by which issuers can signal their quality to receive

dimension]’, Conference proceedings, Gdynia 6 May 1999. Reznikov, AP 1982, Priedskazanije jestiestwiennych processow obuczajuszcziejsja sistiemoj [Forecasting of natural processes using a learning system], Novosibirsk Royal Observatory of Belgium, 2011, http://sidc.oma.be/DATA/index.html Stopa-Boryczka, M, Boryczka, J, Błażek, E, Skrzypczuk, J 1995, Atlas współzależności parametrów meteorologicznych i geograficznych w Polsce [Atlas of correlations between meteorological and geographical parameters in Poland], vol. IX, pp. 320. Stopa-Boryczka, M, Boryczka J 2003, ‛The

REFERENCES [1] J. F. Robeson and W. C. Copacino. The Logistics Handbook , New York, NY: The Free Press, 1994. [2] Y. Wang and B. Tomlin. “To Wait or Not to Wait: Optimal Ordering Under Lead Time Uncertainty and Forecast Updating”. Naval Research Logistics. vol. 56, pp. 766-779, 2009. [3] A. Wieczorek. “Methods and techniques of prediction of key performance indicators for implementation of changes in maintenance organisation”. Management Systems in Production Engineering , vol. 5, pp. 5-9, 2012. [4] T. Berlec, P. Potocnik, E. Govekar, et al. “Forecasting Lead

with ground-based measurements of rainfall using an adaptive multiquadric surface fitting algorithm. Journal of Hydrology. 2013, 500, 84-96. [13] LÖWE, R., THORNDAHL, S., MIKKELSEN, P. S., RASMUSSEN, M. R., MADSEN, H. Probabilistic online runoff forecasting for urban catchments using inputs from rain gauges as well as statically and dynamically adjusted weather radar. Journal of Hydrology, 2014, 512, 397-407. [14] TESCHL, R., RANDEU, W. L., TESCHL, F. Improving weather radar estimates of rainfall using feedforward neural networks. Neural Networks. 2007, 20(4), 519

Abstract

The Romanian Navy missions and actions in Black Sea can lead to ecological risks and the forecast of spill direction based on real data from NOOA. There is an increase in strategic importance of the Black Sea region and the protection of Romanian coastline is a very important task. Chemical spill can now be analyzed with a fast method, called Gnome and predictions can be used with higher accuracy. Forecasts can be used in port protection against offshore spill and as military source of high value information related to Black Sea.

Brdyś, M.T. (2009). Adaptive predictions of stock exchange indices state space wavelet networks, International Journal of Applied Mathematics and Computer Science 19 (2): 337–348, DOI: 10.2478/v10006-009-0029-z. CSO (2014). Foreign Trade in January–December 2013 , Statistical Information and Elaborations, Central Statistical Office, Warsaw. Grossmann, A. and Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape, SIAM Journal on Mathematical Analysis 15 (4): 723–736. Guidolin, M. and Timmermann, A. (2009). Forecasts of