Skip to main content
SpringerLink
Log in

Methodology for Automated Identifying Food Export Potential

  • Conference paper
  • First Online:
Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1223))

Abstract

The food and agriculture could be a driver of the economy in Russia if intensive growth factors were mainly used. In particular, it is necessary to adjust the food export structure to fit reality better. This problem implies long-term forecasting of the commodity combinations and export directions which could provide a persistent export gain in the future. Unfortunately, the existing solutions for food market forecasting tackle mainly with short-term prediction, whereas structural changes in a whole branch of an economy can last during years. Long-term food market forecasting is a tricky one because food markets are quite unstable and export values depend on a variety of different features.

The paper provides a methodology which includes a multi-step data-driven framework for export gain forecasting and an approach for automated evaluation of related technologies. The data-driven framework itself uses multimodal data from various databases to detect these commodities and export directions. We propose a quantile nonlinear autoregressive exogenous model together with pre-filtering to tackle with such long-term prediction tasks. The framework also considers textual information from mass-media to assess political risks related to prospective export directions. The experiments show that the proposed framework provides more accurate predictions then widely used ARIMA model. The expert validation of the obtained result confirms that the methodology could be useful for export diversification.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Awokuse, T.: Does agriculture really matter for economic growth in developing countries? In: 2009 Annual Meeting. Agricultural and Applied Economics Association, Milwaukee, Wisconsin, vol. 49762 (2009)

    Google Scholar 

  2. Devyatkin, D., Otmakhova, Y.: Framework for automated food export gain forecasting. In: CEUR Workshop Proceedings, vol. 2523, pp. 22–33 (2019)

    Google Scholar 

  3. Mor, R.S., Jaiswal, S.K., Singh, S., Bhardwaj, A.: Demand forecasting of the short-lifecycle dairy products. In: Chahal, H., Jyoti, J., Wirtz, J. (eds.) Understanding the Role of Business Analytics, pp. 87–117. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1334-9_6

    Chapter  Google Scholar 

  4. Darekar, A., Reddy, A.: Price forecasting of maize in major states. Maize J. 6(1&2), 1–5 (2017)

    Google Scholar 

  5. Ahumada, H., Cornejo, M.: Forecasting food prices: the case of corn, soybeans and wheat. Int. J. Forecast. 32(3), 838–848 (2016)

    Article  Google Scholar 

  6. Burlankov, S., Ananiev, M., Gazhur, A., Sedova, N., Ananieva, O.: Forecasting the development of agricultural production in the context of food security. Sci. Pap. Ser.-Manag. Econ. Eng. Agric. Rural Dev. 18(3), 45–51 (2018)

    Google Scholar 

  7. Koenker, R., Hallock, K.: Quantile regression. J. Econ. Perspect. 15(4), 143–156 (2001)

    Article  Google Scholar 

  8. Maciejowska, K., Nowotarski, J., Weron, R.: Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging. Int. J. Forecast. 32(3), 957–965 (2016)

    Article  Google Scholar 

  9. Li, G., Xu, S., Li, Z., Sun, Y., Dong, X.: Using quantile regression approach to analyze price movements of agricultural products in China. J. Integr. Agric. 11(4), 674–683 (2012)

    Article  Google Scholar 

  10. Arunraj, N., Ahrens, D.: A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. Int. J. Prod. Econ. 170, 321–335 (2015)

    Article  Google Scholar 

  11. Jaud, M., Kukenova, M., Strieborny, M.: Financial development and sustainable exports: evidence from firm-product data. World Econ. 38(7), 1090–1114 (2015)

    Article  Google Scholar 

  12. Makombe, W., Kropp, J.: The effects of Tanzanian maize export bans on producers’ welfare and food security. In: Selected Paper Prepared for Presentation at the Agricultural & Applied Economics Association, Boston, MA, vol. 333-2016-14428 (2016)

    Google Scholar 

  13. Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining of news-headlines for FOREX market prediction: a Multi-layer Dimension Reduction Algorithm with semantics and sentiment. Expert Syst. Appl. 42(1), 306–324 (2015)

    Article  Google Scholar 

  14. Pannakkong, W., Huynh, V.N., Sriboonchitta, S.: ARIMA versus artificial neural network for Thailand’s cassava starch export forecasting. In: Huynh, V.N., Kreinovich, V., Sriboonchitta, S. (eds.) Causal Inference in Econometrics. SCI, vol. 622, pp. 255–277. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27284-9_16

    Chapter  Google Scholar 

  15. Menezes Jr., J.M.P., Barreto, G.A.: Long-term time series prediction with the NARX network: an empirical evaluation. Neurocomputing 71(16–18), 3335–3343 (2008)

    Article  Google Scholar 

  16. Li, H., Guo, S., Sun, J.: A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl.-Based Syst. 37, 378–387 (2013)

    Article  Google Scholar 

  17. Taylor, J.W.: A quantile regression neural network approach to estimating the conditional density of multiperiod returns. J. Forecast. 19(4), 299–311 (2000)

    Article  Google Scholar 

  18. The atlas of economic complexity. http://atlas.cid.harvard.edu. Accessed 01 July 2019

  19. Hamill, L., Gilbert, N.: Agent-Based Modelling in Economics. Wiley, Hoboken (2015). 246 p.

    Book  Google Scholar 

  20. Deissenberg, C., van der Hoog, S., Herbert, D.: EURACE: a massively parallel agent-based model of the European economy. Appl. Math. Comput. 204(2), 541–552 (2008). https://doi.org/10.1016/j.amc.2008.05.116

    Article  MathSciNet  MATH  Google Scholar 

  21. Abdou, M., Hamill, L., Gilbert, N.: Designing and building an agent-based model. In: Heppenstall, A., Crooks, A., See, L., Batty, M. (eds.) Agent-Based Models of Geographical Systems. Springer, Dordrecht (2012). https://doi.org/10.1007/978-90-481-8927-4

    Chapter  Google Scholar 

  22. UN Comtrade: International Trade Statistics. https://comtrade.un.org/data/. Accessed 28 Apr 2019

  23. Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat/en/. Accessed 28 Apr 2019

  24. International Monetary Foundation. http://www.imf.org/en/Data. Accessed 28 Apr 2019

  25. RP5 Weather Archive. http://rp5.ru. Accessed 28 Apr 2019

  26. Kaggle Russian news dataset for sentiment analysis. https://www.kaggle.com/c/sentiment-analysis-in-russian/overview. Accessed 28 Apr 2019

  27. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  28. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)

    Article  MathSciNet  Google Scholar 

  29. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)

    Google Scholar 

  30. Wang, Y., Huang, M., Zhao, L., Zhu, X.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615. Association for Computational Linguistics, Austin (2016)

    Google Scholar 

  31. Ghosh, S., Vinyals, O., Strope, B.: Contextual LSTM (CLSTM) models for large scale NLP tasks. In: arXiv preprint arXiv:1602.06291. ACM (2016)

  32. Al-Rfou, R., Kulkarni, V., Perozzi, B.: POLYGLOT-NER: massive multilingual named entity recognition. In: Proceedings of the 2015 SIAM International Conference on Data Mining, pp. 586–594. Society for Industrial and Applied Mathematics (2015)

    Google Scholar 

  33. Bengisu, M., Ramzi, N.: Forecasting emerging technologies with the aid of science and technology databases. Technol. Forecast. Soc. Change 73(7), 835–844 (2006)

    Article  Google Scholar 

  34. Sokolov, I., Grigoriev, O., Tikhomirov, I., Suvorov, R., Zhebel, V.: On creating a national system for identifying research and development priorities. Sci. Tech. Inf. Process. 46(1), 14–19 (2019). https://doi.org/10.3103/S0147688219010039

    Article  Google Scholar 

  35. Shvets, A., Devyatkin, D., Sochenkov, I., Tikhomirov, I., Popov, K., Yarygin, K.: Detection of current research directions based on full-text clustering. In: 2015 Science and Information Conference (SAI), pp. 483–488. IEEE (2015)

    Google Scholar 

  36. Railsback, S.F., Grimm, V.: Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press, Princeton (2019)

    MATH  Google Scholar 

  37. Lenta.ru Russian News Dataset. https://github.com/yutkin/Lenta.Ru-News-Dataset. Accessed 28 Apr 2019

Download references

Acknowledgements

The project is supported by the Russian Foundation for Basic Research, project number 18-29-03086. The project is also partially funded by the project “Text mining tools for big data” as a part of the program supporting Technical Leadership Centers of the National Technological Initiative “Center for Big Data Storage and Processing” at the Moscow State University (Agreement with Fund supporting the NTI projects No. 13/1251/2018 11.12.2018).

Author information

Authors and Affiliations

  1. Federal Research Centre “Computer Science and Control” RAS, Moscow, Russia

    Dmitry Devyatkin

  2. Moscow State University, Moscow, Russia

    Yulia Otmakhova

  3. Central Economics and Mathematics Institute RAS, Moscow, Russia

    Yulia Otmakhova

Authors
  1. Dmitry Devyatkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yulia Otmakhova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dmitry Devyatkin .

Editor information

Editors and Affiliations

  1. Kazan Federal University, Kazan, Russia

    Alexander Elizarov

  2. National Research University Higher School of Economics, St. Petersburg, Russia

    Boris Novikov

  3. Federal Research Center “Computer Science and Control”, Moscow, Russia

    Sergey Stupnikov

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Devyatkin, D., Otmakhova, Y. (2020). Methodology for Automated Identifying Food Export Potential. In: Elizarov, A., Novikov, B., Stupnikov, S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2019. Communications in Computer and Information Science, vol 1223. Springer, Cham. https://doi.org/10.1007/978-3-030-51913-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-51913-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51912-4

  • Online ISBN: 978-3-030-51913-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation