Skip to main content

Advertisement

Springer Nature Link
Log in

Prediction of national agricultural products wholesale price index in China using deep learning

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

The national agricultural products wholesale price index (NPI), as a main statistical indicator to reflect and evaluate the states of agricultural products wholesale market in China, can help people keep better track of agricultural products wholesale price changes and regular pattern dynamically. However, the compilation task of NPI is complicated, difficult, labor-consuming and error-prone. Thus, a dual-stage attention-based recurrent neural network (DA-RNN) model is introduced in this work to build a deep learning model for predicting NPI based on the available average prices of major agricultural products. The root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to evaluate the forecasting performance. Experimental results show that the DA-RNN model achieves the superior performance on various evaluation metrics (RMSE = 0.5892, MAE = 0.3604 and MAPE = 0.3091) compared with other deep learning methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Gurland, J., Whittle, P.: Hypothesis testing in time series analysis. J. Am. Stat. Assoc. 49(265), 197 (1954). https://doi.org/10.2307/2281054

    Article  Google Scholar 

  2. Elman, J.L.: Distributed representations, simple recurrent networks, and grammatical structure. Mach. Learn. 7(2–3), 195–225 (1991). https://doi.org/10.1007/BF00114844

    Article  Google Scholar 

  3. Chang, Y., Sun, F., Wu, Y., Lin, S.: A memory-network based solution for multivariate time-series forecasting (2018).

  4. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  5. Chung, J., Gulcehre, C., Hok, C., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014).

  6. Diaconescu, E.: The use of NARX neural networks to predict chaotic time series. WSEAS Trans. Comput. Res. 3(3), 182–191 (2008)

    Google Scholar 

  7. Choi, E., Bahadori, M.T., Sun, J., Kulas J., Schuetz, A., Stewart, W.: Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In: Proceedings of the 2016 NIPS, pp. 3504–3512.

  8. Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches (2014).

  9. Liu, Y., Gong, C., Yang, L., Chen, Y.: DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction. Exp. Syst. Appl. (2020). doi:https://doi.org/10.1016/j.eswa.2019.113082

  10. Qin, Y., Song, D., Cheng, H., Cheng, W., Jiang, G., Cottrell, G.W.: A dual-stage attention-based recurrent neural network for time series prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI). 2017, pp. 2627–2633. doi:https://doi.org/10.24963/ijcai.2017/366

  11. Tao, Y., Ma, L., Zhang, W., Liu, J., Liu, W., Du, Q.: Hierarchical attention-based recurrent highway networks for time series prediction (2018).

  12. Ji, M., Zhang, L., Wu, Q.: Automatic grape leaf diseases identification via United Model based on multiple convolutional neural networks. Inf. Process. Agric. (2019). https://doi.org/10.1016/j.inpa.2019.10.003

    Article  Google Scholar 

  13. Bock, S., Goppold, J., Weiß, M.: An improvement of the convergence proof of the ADAM-Optimizer (2018).

  14. Plutowski, M.E.P., Cottrell, G.W., White, H.: Experience with selecting exemplars from clean data. Neural Netw. 9(2), 273–294 (1996)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Public Welfare Industry (Agriculture) Research Projects Level-2 under Grant 201503116-04-06; Postdoctoral Foundation of Heilongjiang Province under Grant LBHZ15020; Harbin Applied Technology Research and Development Program under Grant 2017RAQXJ096; and National Key Application Research and Development Program in China under Grant 2018YFD0300105-2.

Author information

Authors and Affiliations

  1. College of Engineering, Northeast Agricultural University, Harbin, 150030, China

    Miaomiao Ji, Peng Liu & Zhao Deng

  2. College of Arts and Sciences, Northeast Agricultural University, Harbin, 150030, China

    Qiufeng Wu

Authors
  1. Miaomiao Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhao Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiufeng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiufeng Wu.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Availability of data and material

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The codes used during the current study are available from the corresponding author on reasonable request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ji, M., Liu, P., Deng, Z. et al. Prediction of national agricultural products wholesale price index in China using deep learning. Prog Artif Intell 11, 121–129 (2022). https://doi.org/10.1007/s13748-021-00264-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-021-00264-0

Keywords