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Time series interpolation via global optimization of moments fitting

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  • Carrizosa, Emilio
  • Olivares-Nadal, Alba V.
  • Ramírez-Cobo, Pepa

Abstract

Most time series forecasting methods assume the series has no missing values. When missing values exist, interpolation methods, while filling in the blanks, may substantially modify the statistical pattern of the data, since critical features such as moments and autocorrelations are not necessarily preserved.

Suggested Citation

  • Carrizosa, Emilio & Olivares-Nadal, Alba V. & Ramírez-Cobo, Pepa, 2013. "Time series interpolation via global optimization of moments fitting," European Journal of Operational Research, Elsevier, vol. 230(1), pages 97-112.
    • Handle: RePEc:eee:ejores:v:230:y:2013:i:1:p:97-112
    DOI: 10.1016/j.ejor.2013.04.008
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    References listed on IDEAS

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    Cited by:

    1. Chuanjie Xie & Chong Huang & Deqiang Zhang & Wei He, 2021. "BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data," IJERPH, MDPI, vol. 18(19), pages 1-12, September.
    2. Dong, Qingli & Sun, Yuhuan & Li, Peizhi, 2017. "A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China," Renewable Energy, Elsevier, vol. 102(PA), pages 241-257.
    3. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.

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