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Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model

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  • Xiong, Tao
  • Li, Chongguang
  • Bao, Yukun

Abstract

In view of the importance of interval-valued time series (ITS) modeling and forecasting, and the less research efforts made before, this study proposes an hybrid modeling framework combining interval Holt's exponential smoothing method (HoltI) and multi-output support vector regression (MSVR) for ITS forecasting. Following the philosophy of well-established hybrid “linear and nonlinear” modeling framework, HoltI and MSVR are committed to capture the linear and nonlinear patterns hidden in ITS, respectively. Different from the previous studies considering to model the highs and lows of intervals separately, the proposed hybrid method (termed as HoltI-MSVR) is used to model and forecast the daily highs and lows of ITS simultaneously, taking into account the possible interrelations between the bounds. Three ITS datasets extracted from finance market and energy market are used to compare the prediction performance of the HoltI-MSVR with five selected competitors. The experimental results are judged on the basis of statistical criteria, i.e., the goodness of forecast measure and the accuracy compared to competing forecasts test, and economic criteria, i.e., the returns obtained from a simple trading strategy based on the interval forecasts. The results obtained suggest that the proposed HoltI-MSVR is a promising alternative for ITS forecasting.

Suggested Citation

  • Xiong, Tao & Li, Chongguang & Bao, Yukun, 2017. "Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model," Economic Modelling, Elsevier, vol. 60(C), pages 11-23.
  • Handle: RePEc:eee:ecmode:v:60:y:2017:i:c:p:11-23
    DOI: 10.1016/j.econmod.2016.08.019
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    7. Sun, Shaolong & Sun, Yuying & Wang, Shouyang & Wei, Yunjie, 2018. "Interval decomposition ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 76(C), pages 274-287.
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    10. Gloria Gonzalez‐Rivera & Yun Luo & Esther Ruiz, 2020. "Prediction regions for interval‐valued time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 373-390, June.
    11. OlaOluwa S. Yaya & Xuan Vinh Vo & Ahamuefula E. Ogbonna & Adeolu O. Adewuyi, 2022. "Modelling cryptocurrency high–low prices using fractional cointegrating VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 489-505, January.
    12. Wenyang Huang & Huiwen Wang & Shanshan Wang, 2021. "Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA," Papers 2103.16908, arXiv.org.
    13. Guo, Wei & Liu, Qingfu & Luo, Zhidan & Tse, Yiuman, 2022. "Forecasts for international financial series with VMD algorithms," Journal of Asian Economics, Elsevier, vol. 80(C).
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    15. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Wang, Piao & Tao, Zhifu & Liu, Jinpei & Chen, Huayou, 2023. "Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode," Energy Economics, Elsevier, vol. 118(C).
    17. Zhu, Mengrui & Xu, Hua & Wang, Minggang & Tian, Lixin, 2024. "Carbon price interval prediction method based on probability density recurrence network and interval multi-layer perceptron," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    18. Leandro Maciel & Rosangela Ballini, 2021. "Functional Fuzzy Rule-Based Modeling for Interval-Valued Data: An Empirical Application for Exchange Rates Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 743-771, February.

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