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Comparison between Inverse Model and Chaos Time Series Inverse Model for Long-Term Prediction

Author

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  • Young-Jin Kim

    (Division of Architecture, Architectural Engineering and Civil Engineering, Sunmoon University, Asan, Chungnam 336-708, Korea)

Abstract

This paper presents an inverse model using chaotic behaviour. The chaos time series inverse model, which uses coupling methods between an inverse model and chaos theory can reconstruct a deterministic and low-dimensional phase space by transforming irregular behaviours of nonlinear time-varying systems into a strange attractor (e.g., a Rossler attractor or a Lorenz attractor), and it can then predict future states. For this study, the author used a training dataset measured in an existing high-rise building and examined the predictive abilities of the chaos time series inverse model modelled into phase spaces with strange attractors in comparison with those of the Support Vector Regression (SVR) out of the inverse model. The paper discusses the effective use of the chaos time series inverse model for multi-step ahead prediction.

Suggested Citation

  • Young-Jin Kim, 2017. "Comparison between Inverse Model and Chaos Time Series Inverse Model for Long-Term Prediction," Sustainability, MDPI, vol. 9(6), pages 1-13, June.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:982-:d:100725
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    Cited by:

    1. Lintao Yang & Honggeng Yang & Haitao Liu, 2018. "GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting," Sustainability, MDPI, vol. 10(1), pages 1-16, January.

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