Agris on-line Papers in Economics and Informatics

Faculty of Economics and Management CULS Prague, Kamýcká 129, 165 00 Praha - Suchdol

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The Use of Combined Models in the Construction of Foodstuffs Consumption Forecasting in the Czech Republic

Libuše Svatošová, Jana Köppelová

DOI: 10.7160/aol.2017.090408

Agris on-line Papers in Economics and Informatics, No 4 /2017, December

pp. 81-89

Svatošová, L. and Köppelová, J. (2017) “The Use of Combined Models in the Construction of Foodstuffs Consumption Forecasting in the Czech Republic", AGRIS on-line Papers in Economics and Informatics, Vol. 9, No. 4, pp. 81-89. ISSN 1804-1930.DOI 10.7160/aol.2017.090408.


Many authors all over the world attempt to perform time series analyses (at differing levels of expertise)
in their published works. Knowledge of quantitative information is necessary for decision making in any
domain. Therefore, it is more desirable to enter this field of problems and examine and develop everything
that has been offered by these modern methodologies. In time series forecasting, the extrapolation methods
are applied most frequently in practice. Currently, the combined models have been increasingly employed
in experiments – these represent an aggregation of prognoses obtained from various separate models.
The study presented is aimed at such new approaches, i.e. the construction of combined prediction models
that are more realistic, more flexible and more concise in the time series modelling. This paper focuses
on a subsequent assessment of combined prognoses constructed and a comparison of these with selected
separate models having participated in the aggregate prognoses making. In order to obtain an efficient product,
the Time Series Forecasting System (TSFS) component has been employed, being a component of the SAS
programme system. For quality assessment of the models constructed, the assessment criteria selected
in advance have been applied. The results of this empirical study have shown that in the domain of estimation
of future foodstuffs consumption development, the techniques illustrated in this paper by examples
of long-term time series from foodstuffs consumption area in the Czech Republic (CR), can be employed
with success. This way represents a suitable supplement to complex econometric models.


Foodstuffs consumption in the Czech Republic, time series analysis, exponential smoothing models, Box-Jenkins methodology, combined forecasting models.


  1. Arlt, J. and Arltová, M. (2009) "Ekonomické časové řady", Prague, Professional Publishing. 290 p. ISBN 978-80-86946-85-6.
  2. Cipra, T. (1986) "Analýza časových řad s aplikacemi v ekonomii", Prague, SNTL/Alfa.
  3. Barreras S. A., Sánchez L. E., Figueroa S. F., Olivas, V. J. Á. and Pérez L. C. (2014) "The useof a univariate time series model to short term forecast the behaviour of beef production in BajaCalifornia, Mexico", Veterinaria México, Vol. 45, pp. 1-9. ISSN 03015092.
  4. Deb, Ch., Zhang, F., Yang, J., Lee, S. E. and Shah, K. W. (2017) "A review on time series forecastingtechniques for building energy consumption“, Renewable and Sustainable Energy Reviews, Vol. 74,pp. 902-924. ISSN 13640321.
  5. De Vogli, R., Kouvonen, A. and Gimeno, D. (2014) "The influence of market deregulation on fastfood consumption and body mass index: a cross-national time series analysis", Bulletin of the WorldHealth Organization, Vol. 92, No. 2, pp. 99-107A. DOI 10.2471/BLT.13.120287.
  6. Hindls, R., Hronová, S. and Novák, I. (2000) "Metody statistické analýzy pro ekonomy", Prague.ISBN 80-7261-013-9.
  7. Chen, D., Gao, W., Chen, Y. and Zhang, Q. (2010) "Ecological footprint analysis of food consumptionof rural residents in China in the latest 30 years", Agriculture and Agricultural Science Procedia,Vol. 1, pp. 106-115. ISSN 22107843.
  8. Christodoulos, Ch., Michalakelis, CH. and Varoutas, D. (2011) "On the combinationof exponential smoothing and diffusion forecasts: An application to broadband diffusionin the OECD area“, Technological Forecasting and Social Change, Vol. 78, No. 1, pp. 163-170.ISSN 0040-1625. DOI 10.1016/j.techfore.2010.08.007.
  9. Kába, B. (1997) "Konstrukce kombinovaných předpovědí časových řad ekonomických ukazatelů",in: Zborník vedeckých prác z MVD, Nitra.
  10. Köppelová, J. and Jindrová, A. (2017) "Comparative Study of Short-Term Time Series Models:Use of Mobile Telecommunication Services in CR Regions“, Agris on-line Papers in Economicsand Informatics, Vol. 9, No. 1, pp. 77-89. ISSN 18041930. DOI 10.7160/aol.2017.090107.
  11. Mamat, T., Weimin, D. and Jianhua, X. (2016) "Research of combination prediction modelfor time series: a case study in total power of agriculture machinery“, Journal of Nanjing AgriculturalUniversity, Vol. 39, No. 4, pp. 688-695. ISSN 10002030.
  12. Martin, V., Hurn, S. and Harris, D. (2013) "Econometric modelling with time series", CambridgeBooks. ISBN 978-0-521-19660-4.
  13. Náglová, Z. and Horáková, T. (2016) "Influence of Qualitative Factors on Quantitative Determinantsin the Czech Meat Industry Economy“, AGRIS On-line Papers in Economics and Informatics,Vol. 8, No. 4, pp. 111-123. ISSN 1804-1930. DOI 10.7160/aol.2016.080410.
  14. Notarnicola, B., Tassielli, G., Renzulli, P. A., Castellani, V. and Sala, S. (2016) "Environmentalimpacts of food consumption in Europe“, Journal of Cleaner Production, Vol. 140, pp. 753-765.ISSN 0959-6526. DOI 10.1016/j.jclepro.2016.06.080.
  15. Olsen, S. O. and Tuu, H. H. (2017) "Time perspectives and convenience food consumptionamong teenagers in Vietnam: The dual role of hedonic and healthy eating values“, Food ResearchInternational. ISSN 0963-9969. DOI 10.1016/j.foodres.2017.05.008.
  16. Papagera, A., Ioannou, K., Zaimes, G., Iakovoglou, V. and Simeonidou, M. (2014) “Simulationand Prediction of Water Allocation Using Artificial Neural Networks and a Spatially DistributedHydrological Model”, Agris on-line Papers in Economics and Informatics, Vol. 6, No. 4,pp. 101-111. ISSN 1804-1930.
  17. Pletichová, D. and Gebeltová, Z. (2013) "Development of Market Prices of Agricultural Landwithin the Conditions of the EU“, Agris on-line Papers in Economics and Informatics, Vol. 5,No. 3, pp. 65-78. ISSN 1804-1930.
  18. Reboiro-Jato, M., Glez-Dopazo, J., Glez, D., Laza, R., Gálvez, J. F., Pavón, R. Glez-Peňa, D.and Fdez-Riverola, F. (2011) "Using inductive learning to assess compound feed productionin cooperative poultry farms“, Expert Systems With Applications, Vol. 38, No. 11, pp. 14169-14177.ISSN 09574174.
  19. Sachs, L. (1984) "Applied Statistics". Springer-Verlag, New York.
  20. SAS/ETS User`s Guide, Version 6. (1993) SAS Institute Inc., Cary, USA.
  21. Seger, J. and Hindls, R. (1993) "Statistické metody v ekonomii", Prague: H&H.ISBN 80-85787-26-1.
  22. Smutka, L., Steininger, M. and Miffek, O. (2009) "World agricultural production and consumption“,Agris on-line Papers in Economics and Informatics, Vol. 1, No. 2, pp. 3-12. ISSN 1804-1930.
  23. Tavakkoli, A., Hemmasi, A. H., Talaeipour, M., Bazyar, B. and Tajdini, B. (2015) "Forecastingof Particleboard Consumption in Iran Using Univariate Time Series Models“, Bio Resources,Vol. 10, No. 2, pp. 2032-2043. ISSN 19302126.
  24. Xu, G. and Wang, W. (2010) "Forecasting China's natural gas consumption based on a combinationmodel“, Journal of Natural Gas Chemistry, Vol. 19, No. 5, pp. 493-496. ISSN 10039953.

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