[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Advertisement

Springer Nature Link
Log in

Deep long short-term memory based model for agricultural price forecasting

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Agricultural price forecasting is one of the research hotspots in time series forecasting due to its unique characteristics. In this paper, we developed a deep long short-term memory (DLSTM) based model for the accurate forecasting of a nonstationary and nonlinear agricultural prices series. DLSTM model is a type of deep neural network which is advantageous in capturing the nonlinear and volatile patterns by utilizing both the recurrent architecture and deep learning methodologies together. The study further compares the price forecasting ability of the developed DLSTM model with conventional time-delay neural network (TDNN) and ARIMA models using international monthly price series of maize and palm oil. The empirical results demonstrate the superiority of the developed DLSTM model over other models in terms of various forecasting evaluation criteria like root mean square error, mean absolute percentage error and mean absolute deviation. The DLSTM model also showed dominance over other models in predicting the directional change of those monthly price series. Moreover, the accuracy of the forecasts obtained by all the models is also evaluated using the Diebold–Mariano test and the Friedman test whose results validate that the DLSTM based model has a clear advantage over the other two models.

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
£29.99 /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 includes VAT (United Kingdom)

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

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

Availability of data and materials

The paper uses public domain data.

Code availability

Custom code.

References

  1. Xiong T, Li C, Bao Y (2018) Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: evidence from the vegetable market in China. Neurocomputing 275:2831–2844. https://doi.org/10.1016/j.neucom.2017.11.053

    Article  Google Scholar 

  2. Manogna RL, Mishr AK (2021) Forecasting spot prices of agricultural commodities in India: application of deep-learning models. Intell Syst Account Finance Manag 28(1):72–83. https://doi.org/10.1002/isaf.1487

    Article  Google Scholar 

  3. Wang L, Feng J, Sui X, Chu X, Mu W (2020) Agricultural product price forecasting methods: research advances and trend. Br Food J. https://doi.org/10.1108/BFJ-09-2019-0683

    Article  Google Scholar 

  4. Yu L, Zhao Y, Tang L (2017) Ensemble forecasting for complex time series using sparse representation and neural networks. J Forecast 36(2):122–138. https://doi.org/10.1002/for.2418

    Article  MathSciNet  MATH  Google Scholar 

  5. Box GEP, Reinsel GC, Jenkins GM, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, Hoboken

    MATH  Google Scholar 

  6. Darekar A, Reddy AA (2017) Cotton price forecasting in major producing states. Econ Aff 62(3):373–378

    Article  Google Scholar 

  7. Jadhav V, Reddy BVC, Gaddi GM (2018) Application of ARIMA model for forecasting agricultural prices. J Agric Sci Technol A 19(5):981–992

    Google Scholar 

  8. Mehdizadeh S, Fathian F, Adamowski JF (2019) Hybrid artificial intelligence-time series models for monthly streamflow modeling. Appl Soft Comput 80:873–887. https://doi.org/10.1016/j.asoc.2019.03.046

    Article  Google Scholar 

  9. Weron R (2014) Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int J Forecast 30(4):1030–1081. https://doi.org/10.1016/j.ijforecast.2014.08.008

    Article  Google Scholar 

  10. Makridakis S, Spiliotis E, Assimakopoulos V (2018) Statistical and Machine Learning forecasting methods: concerns and ways forward. PLoS ONE 13(3):e0194889. https://doi.org/10.1371/journal.pone.0194889

    Article  Google Scholar 

  11. Haykin S (2010) Neural networks and learning machines. PHI Learning, New Delhi

    Google Scholar 

  12. Bagherzadeh SA, D’Orazio A, Karimipour A, Goodarzi M, Bach QV (2019) A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: outputs predicted analytically instead of numerically to more accuracy and less costs. Physica A 521:406–415

    Article  Google Scholar 

  13. Shamshirband S, Malvandi A, Karimipour A, Goodarzi M, Afrand M, Petković D, Mahmoodian N (2015) Performance investigation of micro-and nano-sized particle erosion in a 90 elbow using an ANFIS model. Powder Technol 284:336–343

    Article  Google Scholar 

  14. Khosravi R, Rabiei S, Khaki M, Safaei MR, Goodarzi M (2021) Entropy generation of graphene–platinum hybrid nanofluid flow through a wavy cylindrical microchannel solar receiver by using neural networks. J Therm Anal Calorim 1–19

  15. Ghasemi A, Hassani M, Goodarzi M, Afrand M, Manafi S (2019) Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network. Physica A 514:36–45

    Article  Google Scholar 

  16. Wu H, Bagherzadeh SA, D’Orazio A, Habibollahi N, Karimipour A, Goodarzi M, Bach QV (2019) Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for non-Newtonian binary fluids. Physica A 535:122409

    Article  Google Scholar 

  17. Tirumala SS, Narayanan A (2019) Classification and diagnostic prediction of prostate cancer using gene expression and artificial neural networks. Neural Comp Appl 31(11):7539–7548. https://doi.org/10.1007/s00521-018-3589-8

    Article  Google Scholar 

  18. Jha GK, Thulasiraman P, Thulasiram RK (2009) PSO based neural network for time series forecasting. In: Proceedings of the international joint conference on neural networks. Atlanta, USA, pp 1422–1427. https://doi.org/10.1109/IJCNN.2009.5178707

  19. Makridakis S, Spiliotis E, Assimakopoulos V (2020) The M4 competition: 100,000 time series and 61 forecasting methods. Int J Forecast 36(1):54–74. https://doi.org/10.1016/j.ijforecast.2019.04.014

    Article  Google Scholar 

  20. Xu X, Zhang Y (2021) Corn cash price forecasting with neural networks. Comput Electron Agric 184:106120. https://doi.org/10.1016/j.compag.2021106120

    Article  Google Scholar 

  21. Jha GK, Sinha K (2014) Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India. Neural Comput Appl 24:563–571. https://doi.org/10.1007/s00521-012-1264-z

    Article  Google Scholar 

  22. Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning. MIT Press, Cambridge

    MATH  Google Scholar 

  23. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  24. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2:1–127

    Article  Google Scholar 

  25. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003

    Article  Google Scholar 

  26. Merkel G, Povinelli R, Brown R (2018) Short-term load forecasting of natural gas with deep neural network regression. Energies. https://doi.org/10.3390/en11082008

    Article  Google Scholar 

  27. Yu P, Yan X (2020) Stock price prediction based on deep neural networks. Neural Comp Appl 32(6):1609–1628. https://doi.org/10.1007/s00521-019-04212-x

    Article  Google Scholar 

  28. Bianchi FM, Maiorino E, Kampffmeyer MC, Rizzi A, Jenssen R (2017) Properties and training in recurrent neural networks. In: Recurrent neural networks for short-term load forecasting. Springer briefs in computer science. Springer, Cham. https://doi.org/10.1007/978-3-319-70338-1_2

  29. Hochreiter S, Schmidhuber J (1997) LSTM can solve hard long time lag problems. NeurIPS 9:473–479

    Google Scholar 

  30. Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol 561:918–929. https://doi.org/10.1016/j.jhydrol.2018.04.065

    Article  Google Scholar 

  31. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112

  32. Graves A, Jaitly N (2014) Towards end-to-end speech recognition with recurrent neural networks. In: International conference on machine learning, pp 1764–1772

  33. Wang C, Yang H, Bartz C, Meinel C (2016) Image captioning with deep bidirectional LSTMs. In: Proceedings of the 24th ACM international conference on multimedia, pp 988–997

  34. Sagheer A, Kotb M (2019) Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323:203–213. https://doi.org/10.1016/j.neucom.2018.09.082

    Article  Google Scholar 

  35. Jin Z, Yang Y, Liu Y (2019) Stock closing price prediction based on sentiment analysis and LSTM. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04504-2

    Article  Google Scholar 

  36. Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: International conference on machine learning, pp 1310–1318

  37. Ahmadi MH, Mohseni-Gharyehsafa B, Ghazvini M, Goodarzi M, Jilte RD, Kumar R (2020) Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid. J Therm Anal Calorim 139(4):2585–2599. https://doi.org/10.1007/s10973-019-08762-z6789

    Article  Google Scholar 

  38. Peng Y, Parsian A, Khodadadi H, Akbari M, Ghani K, Goodarzi M, Bach QV (2020) Develop optimal network topology of artificial neural network (AONN) to predict the hybrid nanofluids thermal conductivity according to the empirical data of Al2O3–Cu nanoparticles dispersed in ethylene glycol. Physica A 549:124015. https://doi.org/10.1016/j.physa.2019.124015

    Article  Google Scholar 

  39. Karimipour A, Bagherzadeh SA, Goodarzi M, Alnaqi AA, Bahiraei M, Safaei MR, Shadloo MS (2018) Synthesized CuFe2O4/SiO2 nanocomposites added to water/EG: evaluation of the thermophysical properties beside sensitivity analysis & EANN. Int J Heat Mass Transfer 127:1169–1179. https://doi.org/10.1016/j.ijheatmasstransfer.2018.08.112

    Article  Google Scholar 

  40. Bahrami M, Akbari M, Bagherzadeh SA, Karimipour A, Afrand M, Goodarzi M (2019) Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: measure MSEs between targets & ANN for Fe–CuO/Eg–water nanofluid. Physica A 519:159–168. https://doi.org/10.1016/j.physa.2018.12.031

    Article  Google Scholar 

  41. Hewamalage H, Bergmeir C, Bandara K (2021) Recurrent neural networks for time series forecasting: current status and future directions. Int J Forecast 37(1):388–427. https://doi.org/10.1016/j.ijforecast.2020.06.008

    Article  Google Scholar 

  42. Livieris IE, Pintelas E, Pintelas P (2020) A CNN–LSTM model for gold price time-series forecasting. Neural Comp Appl. https://doi.org/10.1007/s00521-020-04867-x

    Article  MATH  Google Scholar 

  43. Zhao Y, Li J, Yu L (2017) A deep learning ensemble approach for crude oil price forecasting. Energy Econ 66:9–16. https://doi.org/10.1016/j.eneco.2017.05.023

    Article  Google Scholar 

  44. Zhu B, Shi X, Chevallier J, Wang P, Wei YM (2016) An adaptive multiscale ensemble learning paradigm for nonstationary and nonlinear energy price time series forecasting. J Forecast 35(7):633–651. https://doi.org/10.1002/for.2395

    Article  MathSciNet  MATH  Google Scholar 

  45. Giwa SO, Sharifpur M, Goodarzi M, Alsulami H, Meyer JP (2021) Influence of base fluid, temperature, and concentration on the thermophysical properties of hybrid nanofluids of alumina–ferrofluid: experimental data, modeling through enhanced ANN, ANFIS, and curve fitting. J Therm Anal Calorim 143(6):4149–4167. https://doi.org/10.1007/s10973-020-09372-w

    Article  Google Scholar 

  46. Jiang W (2021) Applications of deep learning in stock market prediction: recent progress. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2021.115537

    Article  Google Scholar 

  47. Mujeeb S, Javaid N, Ilahi M, Wadud Z, Ishmanov F, Afzal MK (2019) Deep long short-term memory: a new price and load forecasting scheme for big data in smart cities. Sustainability 11(4):987. https://doi.org/10.3390/su11040987

    Article  Google Scholar 

  48. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

  49. Kumar RR, Jha GK (2017) Examining the co-movement between energy and agricultural commodity prices in India. J Indian Soc Agric Stat 71(3):241–252

    MathSciNet  Google Scholar 

  50. Kumar RR, Jha GK, Choudhary K, Mishra DC (2020) Spatial integration and price transmission among major potato markets in India. Indian J Agric Sci 90(3):581–584

    Google Scholar 

  51. Escobari D, Garcia S, Mellado C (2017) Identifying bubbles in Latin American equity markets: Phillips-Perron-based tests and linkages. Emerg Mark Rev 33:90–101. https://doi.org/10.1016/j.ememar.2017.09.001

    Article  Google Scholar 

  52. Choudhary K, Jha GK, Kumar RR, Mishra DC (2019) Agricultural commodity price analysis using ensemble empirical mode decomposition: a case study of daily potato price series. Indian J Agric Sci 89(5):882–886

    Google Scholar 

  53. Alrashed AA, Gharibdousti MS, Goodarzi M, de Oliveira LR, Safaei MR, Bandarra Filho EP (2018) Effects on thermophysical properties of carbon based nanofluids: experimental data, modeling using regression, ANFIS and ANN. Int J Heat Mass Transfer 125:920–932. https://doi.org/10.1016/j.ijheatmasstransfer.2018.04.142

    Article  Google Scholar 

  54. Liang X, Xu J (2021) Biased ReLU neural networks. Neurocomputing 423:71–79. https://doi.org/10.1016/j.neucom.2020.09.050

    Article  Google Scholar 

  55. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  56. Wu J, Hu K, Cheng Y, Zhu H, Shao X, Wang Y (2020) Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network. ISA Trans 97:241–250. https://doi.org/10.1016/j.isatra.2019.07.004

    Article  Google Scholar 

Download references

Acknowledgements

The first author is grateful to University Grants Commission (UGC) for offering the financial assistance and also to PG School, ICAR-Indian Agricultural Research Institute, New Delhi for providing the requisite facilities to carry out this study.

Funding

Partial funding from National Fellowship for OBC students of University Grants Commission (UGC), India for the first author.

Author information

Authors and Affiliations

  1. ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, 110012, India

    Ronit Jaiswal, Rajeev Ranjan Kumar & Kapil Choudhary

  2. ICAR-Indian Agricultural Research Institute, Pusa, New Delhi, 110012, India

    Girish K. Jha

Authors
  1. Ronit Jaiswal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Girish K. Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajeev Ranjan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kapil Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Girish K. Jha.

Ethics declarations

Competing interests

The authors have no potential conflicts to report that are important to the article's content.

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

Jaiswal, R., Jha, G.K., Kumar, R.R. et al. Deep long short-term memory based model for agricultural price forecasting. Neural Comput & Applic 34, 4661–4676 (2022). https://doi.org/10.1007/s00521-021-06621-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06621-3

Keywords

Search

Navigation