Skip to main content
SpringerLink
Log in

A Support Vector Based Hybrid Forecasting Model for Chaotic Time Series: Spare Part Consumption Prediction

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Reliability of spare parts inventory in the company is one of the most significant challenges in the field of maintenance and repairs, but on the other hand, the liquidity crisis resulting from the purchase of surplus spare parts is another challenge facing the organizational financial field. Accordingly, accurate forecasting of future consumption is one of the most important solutions for inventory control systems. But because of the impact of so many variables on spare part consumption, most real-world data is chaotic. This leads to the use of classical methods to predict future demand, with high error and low reliability. In this research, a novel and reliable hybrid model based on the support vector machine (SVM), and two single algorithms (STL Decomposed ARIMA and three-layer feed-forward neural network) has been presented to predict the future consumption of spare parts. The proposed model (SVM-ARIMA-3LFFNN hybrid model) also experiments on several chaotic time series in the rapid miner repositories. The forecasting results indicate that the proposed hybrid model attains superior performance compared with a single model and can adapt to chaotic time series. Performance criteria considered in this study are MAE, RMSE, MAPE, and sMAPE. The results indicate that the proposed model can improve the RMSE, MAPE, and sMAPE (up to 30% improvement).

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

Similar content being viewed by others

Notes

  1. Autoregressive.

  2. Autoregressive moving average.

  3. Autoregressive integrated moving average.

  4. Mean absolute percentage error.

  5. Symmetric Mean absolute percentage error.

  6. Root mean squared error.

  7. Mean absolute error.

  8. Seasonal and Trend decomposition using Loess.

  9. A regularization factor constrains the absolute value of the weights and has the net effect of dropping some weights (setting them to zero) from a model to reduce complexity and avoid overfitting.

  10. A regularization factor that constrains the sum of the squared weights. This method introduces bias into parameter estimates but frequently produces substantial gains in modeling as estimate variance is reduced.

  11. Sum Squared Error (SSE): This metric uses for measuring the separation level intra-clusters.

  12. Gini Coefficient: This metric uses for measuring the correlation level inter clusters.

References

  1. Dombi J, Jónás T, Eszter Tóth Z (2018) Modeling and long-term forecasting demand in spare parts logistics businesses. Int J Prod Econ. https://doi.org/10.1016/j.ijpe.2018.04.015

    Article  Google Scholar 

  2. Hua Z, Zhang B (2006) A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Appl Math Comput 181(2):1035–1048. https://doi.org/10.1016/j.amc.2006.01.064

    Article  MathSciNet  MATH  Google Scholar 

  3. Guo F, Diao J, Zhao Q, Wang D, Sun Q (2017) A double-level combination approach for demand forecasting of repairable airplane spare parts based on turnover data. Comput Ind Eng. https://doi.org/10.1016/j.cie.2017.05.002

    Article  Google Scholar 

  4. McGovern A, Rosendahl D, Brown R (2011) Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction. Data Min Knowl Disc 22(1–2):232–258. https://doi.org/10.1007/s10618-010-0193-7

    Article  Google Scholar 

  5. Bozic M, Stojanovic M, Stajic Z (2013) Mutual information-based inputs selection for electric load time series forecasting. Entropy 15(3):926–942. https://doi.org/10.3390/e15030926

    Article  MathSciNet  Google Scholar 

  6. Lukinskiy V, Lukinskiy V, Sokolov B (2020) Control of inventory dynamics: A survey of special cases for products with low demand. Ann Rev Cont. https://doi.org/10.1016/j.arcontrol.2020.04.005

    Article  MathSciNet  Google Scholar 

  7. Niu H, Wang J (2014) Financial time series prediction by a random data-time ef- fective RBF neural network. Soft Comput 18(3):497–508. https://doi.org/10.1007/s00500-013-1070-2

    Article  Google Scholar 

  8. Tian ZD (2020) Chaotic characteristic analysis of network traffic time series at different time scales. Chaos, Solitons Fractals 130:109412. https://doi.org/10.1016/j.chaos.2019.109412

    Article  MathSciNet  MATH  Google Scholar 

  9. Chandra R, Zhang M (2012) Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 86(1):116–123. https://doi.org/10.1016/j.neucom.2012.01.014

    Article  Google Scholar 

  10. Wang X, Han M (2014) Online sequential extreme learning machine with kernels for nonstationary time series prediction. Neurocomputing 145(5):90–97. https://doi.org/10.1016/j.neucom.2014.05.068

    Article  Google Scholar 

  11. Tian Z, Gao X, Shi T (2014) Combination kernel function least-squares support vector machine for chaotic time series prediction. Acta Physica Sinica. https://doi.org/10.7498/aps.63.160508

    Article  Google Scholar 

  12. Tang LH, Bai YL, Yang J, Lu YN (2020) A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series. Chaos, Solit, Fract. https://doi.org/10.1016/j.chaos.2020.110366

    Article  MathSciNet  Google Scholar 

  13. Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput 11(2):2664–2675. https://doi.org/10.1016/j.asoc.2010.10.015

    Article  Google Scholar 

  14. Bai Y, Tang L, Fan M, Ma X, Yang Y (2020) Fuzzy first-order transition-rules-trained hybrid forecasting system for short-term wind speed forecasts. Energies. https://doi.org/10.3390/en13133332

    Article  Google Scholar 

  15. Liu Z (2010) Chaotic time series analysis. Math Probl Eng 2010:31. https://doi.org/10.1155/2010/720190

    Article  MathSciNet  MATH  Google Scholar 

  16. Davies B (2005) Exploring chaos: theory and experiment. Perseus Books, New York

    Google Scholar 

  17. Chandra R (2015) Competition and collaboration in cooperative coevolution of elman recurrent neural networks for time series prediction. IEEE Trans Neur Netw Learn Sys 26(12):3123–3136. https://doi.org/10.1109/TNNLS.2015.2404823

    Article  MathSciNet  Google Scholar 

  18. Osaka M (2000) Local box-counting to determine the fractal dimension of high-order chaos. Int J Mod Phys 11(8):1519–1526

    Article  MATH  Google Scholar 

  19. Mackey MC, Glass L (1977) Oscillation and chaos in physiological control systems. Sci Comput Program 197(4300):287–289

    MATH  Google Scholar 

  20. Egrioglu E, Aladag CH, Yolcu U (2013) Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst Appl 40(3):854–857. https://doi.org/10.1016/j.eswa.2012.05.040

    Article  Google Scholar 

  21. Guo W, Xu T, Lu Z (2016) An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution. Neural Comput Appl 16(27):883–898. https://doi.org/10.1007/s00521-015-1903-2

    Article  Google Scholar 

  22. Tian ZD, Li SJ, Wang YH, Sha Y (2014) A prediction method based on wavelet transform and multiple models fusion for chaotic time series. Chaos, Solitons Fract. https://doi.org/10.1016/j.chaos.2017.03.018

    Article  MATH  Google Scholar 

  23. Ganjefar S, Tofighi M (2018) "Optimization of quantum-inspired neural network using a memetic algorithm for function approximation and chaotic time series prediction," Neurocomputing, vol. 291, no. 1, pp. 175–186, 2018, https://www.onacademic.com/detail/journal_1000040228116710_9b0d.html.

  24. Nguyen H, Kalra G, Jun T (2019) Chaotic time series prediction using a novel Echo State Network model with inputs reconstruction, Bayesian ridge regression, and independent component analysis. Int J Patt Recogn Artif Intell. https://doi.org/10.1142/S0218001420510088

    Article  Google Scholar 

  25. Yang H-F, Phoebe Chen Y-P (2019) Hybrid deep learning and empirical mode decomposition model for time series applications. Expert Sys Appl 120:128–138

    Article  Google Scholar 

  26. Wang R, Peng C, Gao J (2020) A dilated convolution network-based LSTM model for multi-step prediction of chaotic time series. Comput Appl Math. https://doi.org/10.1007/s40314-019-1006-2

    Article  MathSciNet  MATH  Google Scholar 

  27. Tian Z, Li S, Wang Y (2020) A prediction approach using ensemble empirical mode decomposition-permutation entropy and regularized extreme learn- ing machine for short-term wind speed. Wind Energy 23(2):177–206. https://doi.org/10.1002/we.2422

    Article  Google Scholar 

  28. Tian ZD (2020) Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103573

    Article  Google Scholar 

  29. Tian ZD (2020) Kernel principal component analysis-based least squares support vector machine optimized by improved grey wolf optimization algorithm and application in dynamic liquid level forecasting of beam pump. Trans Inst Meas Control 42(6):1135–1150. https://doi.org/10.1177/0142331219885273

    Article  Google Scholar 

  30. Jiang P, Huang Y, Liu X (2021) Intermittent demand forecasting for spare parts in the heavy-Duty Vehicle Industry: a support vector machine model. Int J Prod Resea. https://doi.org/10.1080/00207543.2020.1842936

    Article  Google Scholar 

  31. Boukhtouta A, Jentsch P (2018) "Support vector machine for demand forecasting of Canadian armed forces spare parts," In: 6th International symposium on computational and business intelligence (ISCBI), Basel, Switzerland, 2018: IEEE, pp. 59–64, DOI: https://doi.org/10.1109/ISCBI.2018.00021

  32. Gajendra KV, Chinmoy P, Elsawah AM (2021) A hybrid feedforward neural network algorithm for detecting outliers in non-stationary multivariate time series. Exp Sys Appl. https://doi.org/10.1016/j.eswa.2021.115545

    Article  Google Scholar 

  33. Cleveland RB, Cleveland WS, MacRae JE, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on loess. J Off Stat 6(1):3–73

    Google Scholar 

  34. Goodfellow I, Bengio Y, Courville A (2016) Deep learning (adaptive computation and machine learning series). The MIT Press, New York University

    MATH  Google Scholar 

  35. Wang JZ, Wang SQ, Yang WD (2019) A novel non-linear combination system for short-term wind speed forecast. Energy Built Environ 143:1172–1192

    Google Scholar 

  36. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd ed. Morgan Kaufmann

Download references

Acknowledgements

This work is supported by TakBon company (Decision Support Systems Engineering Company: http://takbon.biz/) and Isfahan university of technology.

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Saba Sareminia

  2. Member of Business Intelligence and Knowledge Management Research Group, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Saba Sareminia

Authors
  1. Saba Sareminia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

I give my consent for the publication of identifiable details, which can include a photograph(s) and/or case history and/or details within the text (“Material”) to be published in your Journal and Article.

Corresponding author

Correspondence to Saba Sareminia.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Ethical approval and consent to participate

I hereby accept the terms of your Journal’s ethical codes. The author did not collect any personal information.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sareminia, S. A Support Vector Based Hybrid Forecasting Model for Chaotic Time Series: Spare Part Consumption Prediction. Neural Process Lett 55, 2825–2841 (2023). https://doi.org/10.1007/s11063-022-10986-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-022-10986-4

Keywords

Search

Navigation