Prediction of construction and assembly production in the province of Lower Silesia. Part I

Magdalena Rogalska


Department of Construction Methods and Management; Faculty of Civil Engineering and Architecture; Lublin University of Technology (Poland)
https://orcid.org/0000-0001-8408-3242

Abstract

The article is the first part of the series „Prediction construction and assembly production in Lower Silesia.” It was assumed that salary of employees will be one of the independent variables to determine the volume of production. Salaries of employees was predicted, using multiple regression and autoregressive moving average SARIMA methods. An analysis of the results was carried out.

The errors ME, MAE, MPE, MAPE and Theil coefficients I, I2, I12, I22, I32 were calculated. Multiple regression equation with 12 predictors was set. Predictors were selected from among the 53 independent variables. Forecasted data were obtained for construction and assembly production prediction.


Keywords:

prediction, multiple regression, SARIMA, salaries of employees

Box, G.E.P., Pierce, D.A., Distribution of residual autocorrelations in autoregressive-integrated moving average time series models, Journal of the American Statistical Association 65 (1970) 1509-26.
DOI: https://doi.org/10.1080/01621459.1970.10481180   Google Scholar

Christodoulos Ch., Michalakelis Ch., Varoutas D., Forecasting with limited data: Combining ARIMA and diffusion models, Technological Forecasting and Social Change 77 (2010) 558-565.
DOI: https://doi.org/10.1016/j.techfore.2010.01.009   Google Scholar

Cieślak M., Prognozowanie gospodarcze : metody i zastosowanie, Wydaw. Naukowe PWN, Warszawa 2001.
  Google Scholar

Dickey D.A., Fuller W.A., Likelihood ratio statistics for autoregressive time series with a unit root, Econometrica 49(4) (1981) 1957-72.
DOI: https://doi.org/10.2307/1912517   Google Scholar

Ediger V.E., Akar S., ARIMA forecasting of primary energy demand by fuel in Turkey, Energy Policy 35(3) (2007) 1701-1708.
DOI: https://doi.org/10.1016/j.enpol.2006.05.009   Google Scholar

Faruk D.O., A hybrid neural network and ARIMA model for water quality time series prediction, Engineering Applications of Artificial Intelligence 23(4) (2010) 586-594.
DOI: https://doi.org/10.1016/j.engappai.2009.09.015   Google Scholar

Gilbert K.C., Chatpattananan V., An ARIMA supply chain model with a generalized ordering policy, Journal of Modelling in Management 1(1) (2006).
DOI: https://doi.org/10.1108/17465660610667793   Google Scholar

Gilbert K.C., An ARIMA supply chain model, Management Science 51(2) (2005) 305-10.
DOI: https://doi.org/10.1287/mnsc.1040.0308   Google Scholar

Kot S., Jakubowski J., Sokołowski A., Statystyka, Difin, Warszawa 2007.
  Google Scholar

Stanisz A., Przystępny kurs statystyki z zastosowaniem STATISTICA PL na przykładach z medycyny,T 1. StatSoft Polska Sp. z o.o., Kraków 2006.
  Google Scholar


Published
2012-12-11

Cited by

Rogalska, M. (2012) “Prediction of construction and assembly production in the province of Lower Silesia. Part I”, Budownictwo i Architektura, 11(2), pp. 121–137. doi: 10.35784/bud-arch.2224.

Authors

Magdalena Rogalska 

Department of Construction Methods and Management; Faculty of Civil Engineering and Architecture; Lublin University of Technology Poland
https://orcid.org/0000-0001-8408-3242

Statistics

Abstract views: 196
PDF downloads: 83


License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Budownictwo i Architektura supports the open science program. The journal enables Open Access to their publications. Everyone can view, download and forward articles, provided that the terms of the license are respected.

Publishing of articles is possible after submitting a signed statement on the transfer of a license to the Journal.