IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v98y2017icp158-172.html
   My bibliography  Save this article

A prediction method based on wavelet transform and multiple models fusion for chaotic time series

Author

Listed:
  • Zhongda, Tian
  • Shujiang, Li
  • Yanhong, Wang
  • Yi, Sha

Abstract

In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.

Suggested Citation

  • Zhongda, Tian & Shujiang, Li & Yanhong, Wang & Yi, Sha, 2017. "A prediction method based on wavelet transform and multiple models fusion for chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 158-172.
    • Handle: RePEc:eee:chsofr:v:98:y:2017:i:c:p:158-172
    DOI: 10.1016/j.chaos.2017.03.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077917300747
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2017.03.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chongli Di & Xiaohua Yang & Xiaochao Wang, 2014. "A Four-Stage Hybrid Model for Hydrological Time Series Forecasting," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-18, August.
    2. Zhaosheng Yang & Qichun Bing & Ciyun Lin & Nan Yang & Duo Mei, 2014. "Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, August.
    3. Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.
    4. Dushmanta Dutta & Wendy Welsh & Jai Vaze & Shaun Kim & David Nicholls, 2012. "A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4397-4415, December.
    5. Buonocore, A. & Caputo, L. & Nobile, A.G. & Pirozzi, E., 2014. "Gauss–Markov processes in the presence of a reflecting boundary and applications in neuronal models," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 799-809.
    6. Guo-feng Fan & Shan Qing & Hua Wang & Zhe Shi & Wei-Chiang Hong & Lin Dai, 2012. "Study on Apparent Kinetic Prediction Model of the Smelting Reduction Based on the Time-Series," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-15, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Yun & Song, Mengmeng & Yang, Dazhi, 2024. "Local-global feature-based spatio-temporal wind speed forecasting with a sparse and dynamic graph," Energy, Elsevier, vol. 289(C).
    2. Ravi Kumar, Rajagounder, 2022. "Adaptive wavelet output regulation for nonlinear systems," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    3. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    4. Fu, Ke & Li, He & Deng, Pengfei, 2022. "Chaotic time series prediction using DTIGNet based on improved temporal-inception and GRU," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xue-hua Zhao & Xu Chen, 2015. "Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2913-2926, June.
    2. Xinxin He & Jungang Luo & Ganggang Zuo & Jiancang Xie, 2019. "Daily Runoff Forecasting Using a Hybrid Model Based on Variational Mode Decomposition and Deep Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1571-1590, March.
    3. Babak Mohammadi & Farshad Ahmadi & Saeid Mehdizadeh & Yiqing Guan & Quoc Bao Pham & Nguyen Thi Thuy Linh & Doan Quang Tri, 2020. "Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3387-3409, August.
    4. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    5. Mostafa Dastorani & Mohammad Mirzavand & Mohammad Taghi Dastorani & Seyyed Javad Sadatinejad, 2016. "Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1811-1827, April.
    6. Kulmus, Kathrin & Essex, Christopher & Prehl, Janett & Hoffmann, Karl Heinz, 2019. "The entropy production paradox for fractional master equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1370-1378.
    7. Amico, Mailis & Van Keilegom, Ingrid, 2017. "Cure models in survival analysis," LIDAM Discussion Papers ISBA 2017007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Yuanyuan Zhou & Min Zhou & Qing Xia & Wei-Chiang Hong, 2019. "Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory," Mathematics, MDPI, vol. 7(12), pages 1-23, December.
    9. Mohammed Seyam & Faridah Othman, 2014. "The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2583-2597, July.
    10. J. Teng & J. Vaze & D. Dutta & S. Marvanek, 2015. "Rapid Inundation Modelling in Large Floodplains Using LiDAR DEM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2619-2636, June.
    11. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    12. David Robertson & Q. Wang, 2013. "Seasonal Forecasts of Unregulated Inflows into the Murray River, Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2747-2769, June.
    13. Yan-Fang Sang, 2013. "Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2807-2821, June.
    14. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.
    15. Dirick, Lore & Claeskens, Gerda & Vasnev, Andrey & Baesens, Bart, 2022. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Econometrics and Statistics, Elsevier, vol. 22(C), pages 39-55.
    16. Li, Libo, 2018. "Predicting online invitation responses with a competing risk model using privacy-friendly social event data," European Journal of Operational Research, Elsevier, vol. 270(2), pages 698-708.
    17. Wei Zhang & Yan Zhu & Xuejun Wang, 2014. "A Modeling Method to Evaluate the Management Strategy of Urban Storm Runoff," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 541-552, January.
    18. Fang-Fang Li & Zhi-Yu Wang & Xiao Zhao & En Xie & Jun Qiu, 2019. "Decomposition-ANN Methods for Long-Term Discharge Prediction Based on Fisher’s Ordered Clustering with MESA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3095-3110, July.
    19. Correale, T.G. & Monteiro, L.H.A., 2016. "On the dynamics of axonal membrane: Ion channel as the basic unit of a deterministic model," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 292-302.
    20. Hafiza Mamona Nazir & Ijaz Hussain & Muhammad Faisal & Alaa Mohamd Shoukry & Showkat Gani & Ishfaq Ahmad, 2019. "Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis," Complexity, Hindawi, vol. 2019, pages 1-14, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:98:y:2017:i:c:p:158-172. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.