Skip to main content

Advertisement

SpringerLink
Log in

On deep ensemble CNN–SAE based novel agro-market price forecasting

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

The prices of agro-commodities are highly volatile. Hence it is a challenge to the farmers to ensure fair and remunerative prices of these commodities. As a result, there is a need for prediction of agro market price appropriately. The closing price prediction of one soft commodity product, Cotton 29 mm and one agro-commodity product, Guar gum are chosen. The existing reported methods exhibit poor prediction performance. To alleviate this problem, the current investigation is undertaken for better prediction of closing prices. The deep ensemble approach using convolutional neural network (CNN) and stacked autoencoder (SAE) is employed to improve the prediction performance. For the ensemble strategy, the weights are optimized using three bio-inspired techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and spider monkey optimization (SMO). Eighteen attributes relating to the closing price of each products are considered as input to the proposed models. The simulation based experimental results demonstrate the following contribution of the paper. Firstly, it is observed that CNN outperforms the SAE model in terms of short range prediction and vice versa for long range prediction. Secondly, the prediction performance of all the three ensemble models has been determined. Thirdly, out of three ensemble models, ensemble-SMO (ESMO) shows the best prediction performance in terms of mean square error and coefficient of multiple determination (R2). It is then followed by ensemble-PSO and ensemble-GA respectively. The performance of proposed best ESMO is compared with the Grey wolf optimization based multiquadratic kernel KELM model (GWO-KELM) and it is observed that the proposed ESMO outperforms the GWO-KELM model.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Banerjee D, Ghosal A, Mukherjee I (2019) Prediction of gold price movement using geopolitical risk as a factor. Emerg Technol Data Min Inf Secur. https://doi.org/10.1007/978-981-13-1501-5_77

    Article  Google Scholar 

  2. Henrique B-M, Sobreiro V-A, Kimura H (2019) Literature review: machine learning techniques applied to financial market prediction. Expert Syst Appl 124:226–251. https://doi.org/10.1016/j.eswa.2019.01.012

    Article  Google Scholar 

  3. Xiao Y, Xiao J, Lu F, Wang S (2014) Ensemble ANNs-PSO-GA approach for day-ahead stock e-exchange prices forecasting. Int J Comput Intell Syst 7(2):272–290. https://doi.org/10.1080/18756891.2013.864472

    Article  Google Scholar 

  4. 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 

  5. Asrari A, Wu T-X, Ramos B (2016) A hybrid algorithm for short-term solar power prediction—Sunshine state case study. IEEE Trans Sustain Energy 8(2):582–591. https://doi.org/10.1109/TSTE.2016.2613962

    Article  Google Scholar 

  6. Tong C, Li J, Lang C, Kong F, Niu J, Rodrigues J-J-P-C (2018) An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders. J Parallel Distrib Comput 117:267–273. https://doi.org/10.1016/j.jpdc.2017.06.007

    Article  Google Scholar 

  7. Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12(7):e0180944. https://doi.org/10.1371/journal.pone.0180944

    Article  Google Scholar 

  8. Doering J, Fairbank M, Markose S (2017) Convolutional neural networks applied to high-frequency market microstructure forecasting. In: 2017 9th computer science and electronic engineering (CEEC). IEEE, pp 31–36. https://doi.org/10.1109/CEEC.2017.8101595

  9. Abbas Q, Ibrahim M-EA, Jaffar M-A (2019) A comprehensive review of recent advances on deep vision systems. Artif Intell Rev 52(1):39–76. https://doi.org/10.1007/s10462-018-9633-3

    Article  Google Scholar 

  10. Lago J, Ridder F-D, Schutter B-D (2018) Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms. Appl Energy 221:386–405. https://doi.org/10.1016/j.apenergy.2018.02.069

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  12. Kusuma R-M-I, Ho T-T, Kao W-C, Ou Y-Y, Hua K-L (2019) Using deep learning neural networks and candlestick chart representation to predict stock market. arXiv preprint arXiv:1903.12258

  13. Gunduz H, Yaslan Y, Cataltepe Z (2017) Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations. Knowl Based Syst 137:138–148. https://doi.org/10.1016/j.knosys.2017.09.023

    Article  Google Scholar 

  14. Hoseinzade E, Haratizadeh S (2019) CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Syst Appl 129:273–285. https://doi.org/10.1016/j.eswa.2019.03.029

    Article  Google Scholar 

  15. Liu C, Hou W, Liu D (2017) Foreign exchange rates forecasting with convolutional neural network. Neural Process Lett 46:1095–1119. https://doi.org/10.1007/s11063-017-9629-z

    Article  Google Scholar 

  16. Luo Z, Cai X, Tanaka K, Takiguchi T, Kinkyo T, Hamori S (2019) Can we forecast daily oil futures prices? Experimental evidence from convolutional neural networks. J Risk Financ Manag 12(1):9. https://doi.org/10.3390/jrfm12010009

    Article  Google Scholar 

  17. Heaton J-B, Polson N-G, Witte J-H (2017) Deep learning for finance: deep portfolios. Appl Stoch Models Bus Ind 33(1):3–12. https://doi.org/10.1002/asmb.2209

    Article  MathSciNet  MATH  Google Scholar 

  18. Shamshirband S, Nodoushan E-J, Adolf J-E, Manaf A-A, Mosavi A, Chau K-W (2019) Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Eng Appl Comput Fluid Mech 13(1):91–101. https://doi.org/10.1080/19942060.2018.1553742

    Article  Google Scholar 

  19. Du P (2018) Ensemble machine learning-based wind forecasting to combine NWP output with data from weather station. IEEE Trans Sustain Energy 10(4):2133–2141. https://doi.org/10.1109/TSTE.2018.2880615

    Article  Google Scholar 

  20. Al-Fattah S-M (2019) Artificial intelligence approach for modeling and forecasting oil-price volatility. SPE Reserv Eval Eng. https://doi.org/10.2118/195584-PA

    Article  Google Scholar 

  21. Al-Rakhami M, Gumaei A, Alsanad A, Alamri A, Hassan M-M (2019) An ensemble learning approach for accurate energy load prediction in residential buildings. IEEE Access 7:48328–48338. https://doi.org/10.1109/ACCESS.2019.2909470

    Article  Google Scholar 

  22. Yu L, Dai W, Tang L (2016) A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting. Eng Appl Artif Intell 47:110–121. https://doi.org/10.1016/j.engappai.2015.04.016

    Article  Google Scholar 

  23. Xian L, He K, Lai K-K (2016) Gold price analysis based on ensemble empirical model decomposition and independent component analysis. Physica A 454:11–23. https://doi.org/10.1016/j.physa.2016.02.055

    Article  Google Scholar 

  24. Sideratos G, Ikonomopoulos A, Hatziargyriou N-D (2020) A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks. Electr Power Syst Res 178:106025. https://doi.org/10.1016/j.epsr.2019.106025

    Article  Google Scholar 

  25. Anish CM, Majhi B, Majhi R (2016) Development and evaluation of novel forecasting adaptive ensemble model. J Finance Data Sci 2(3):188–201. https://doi.org/10.1016/j.jfds.2016.12.002

    Article  MATH  Google Scholar 

  26. Chung H, Shin K (2020) Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction. Neural Comput Appl 32:7897–7914. https://doi.org/10.1007/s00521-019-04236-3

    Article  Google Scholar 

  27. Li S, Wang P, Goel L (2015) A novel wavelet-based ensemble method for short-term load forecasting with hybrid neural networks and feature selection. IEEE Trans Power Syst 31(3):1788–1798. https://doi.org/10.1109/TPWRS.2015.2438322

    Article  Google Scholar 

  28. Nadeem F, Alghazzawi D, Mashat A, Faqeeh K, Almalaise A (2019) Using machine learning ensemble methods to predict execution time of e-science workflows in heterogeneous distributed systems. IEEE Access 7:25138–25149. https://doi.org/10.1109/ACCESS.2019.2899985

    Article  Google Scholar 

  29. Parida N, Mishra D, Das K, Rout N-K (2019) Development and performance evaluation of hybrid KELM models for forecasting of agro-commodity price. Evol Intel. https://doi.org/10.1007/s12065-019-00295-6

    Article  Google Scholar 

  30. Singh V, Swaminathan A, Verma N-K (2019) Convolutional neural network with stacked autoencoder for kernel initialization. In: Verma N, Ghosh A (eds) Computational intelligence: theories, applications and future directions-volume II, vol 799. Springer, Singapore, pp 53–63. https://doi.org/10.1007/978-981-13-1135-2_5

    Chapter  Google Scholar 

  31. Chen J, Zhu Q, Li H, Zhu L, Shi D, Li Y, Duan X, Liu Y (2019) Learning heterogeneous features jointly: a deep end-to-end framework for multi-step short-term wind power prediction. IEEE Trans Sustain Energy 11(3):1761–1772. https://doi.org/10.1109/TSTE.2019.2940590

    Article  Google Scholar 

  32. Holland J-H (1992) Genetic algorithms. Sci Am 267(1):66–73. https://doi.org/10.2307/24939139

    Article  Google Scholar 

  33. Bansal J-C, Sharma H, Jadon S-S, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memet Comput 6(1):31–47. https://doi.org/10.1007/s12293-013-0128-0

    Article  Google Scholar 

  34. Agrawal V, Rastogi R, Tiwari D-C (2018) Spider monkey optimization: a survey. Int J Syst Assur Eng Manag 9(4):929–941. https://doi.org/10.1007/s13198-017-0685-6

    Article  Google Scholar 

  35. Gupta K, Deep K, Nagar A-K (2019) Application of constrained spider monkey optimization to solve portfolio optimization problem. In: Deep K, Jain M, Salhi S (eds) Decision science in action. Springer, Singapore, pp 175–191. https://doi.org/10.1007/978-981-13-0860-4_13

    Chapter  Google Scholar 

  36. Sharma H, Hazrati G, Bansal J-C (2019) Spider monkey optimization algorithm. In: Bansal J, Singh P, Pal N (eds) Evolutionary and swarm intelligence algorithms, vol 779. Springer, Singapore, pp 43–59. https://doi.org/10.1007/978-3-319-91341-4_4

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

    Nirjharinee Parida, Debahuti Mishra & Kaberi Das

  2. Department of Computer Application, National Institute of Technology, Raipur, G.E. Road, Raipur, Chhatisgarh (C.G), 492010, India

    Narendra Kumar Rout

  3. Department of Electronics and Telecommunication Engineering, C.V. Raman Global University, Bhubaneswar, Odisha, 752054, India

    Ganapati Panda

Authors
  1. Nirjharinee Parida
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debahuti Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kaberi Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Narendra Kumar Rout
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ganapati Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nirjharinee Parida.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests about the publication of this research paper.

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

Parida, N., Mishra, D., Das, K. et al. On deep ensemble CNN–SAE based novel agro-market price forecasting. Evol. Intel. 14, 851–862 (2021). https://doi.org/10.1007/s12065-020-00466-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-020-00466-w

Keywords

Search

Navigation