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Modulated cycles, an approach to modelling periodic components from rapidly sampled data

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  • Pedregal, Diego J.
  • Young, Peter C.

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  • Pedregal, Diego J. & Young, Peter C., 2006. "Modulated cycles, an approach to modelling periodic components from rapidly sampled data," International Journal of Forecasting, Elsevier, vol. 22(1), pages 181-194.
    • Handle: RePEc:eee:intfor:v:22:y:2006:i:1:p:181-194
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    References listed on IDEAS

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    1. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    2. Peter Young, 1999. "Recursive and en-bloc approaches to signal extraction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(1), pages 103-128.
    3. Henley, Andrew & Peirson, John, 1998. "Residential energy demand and the interaction of price and temperature: British experimental evidence," Energy Economics, Elsevier, vol. 20(2), pages 157-171, April.
    4. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
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    1. D J Pedregal & P C Young, 2008. "Development of improved adaptive approaches to electricity demand forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1066-1076, August.
    2. Proietti, Tommaso & Pedregal, Diego J., 2023. "Seasonality in High Frequency Time Series," Econometrics and Statistics, Elsevier, vol. 27(C), pages 62-82.
    3. Villegas, Marco A. & Pedregal, Diego J., 2019. "Automatic selection of unobserved components models for supply chain forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 157-169.
    4. Rodríguez, Alejandro & Ruiz, Esther, 2012. "Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 62-74, January.
    5. D Laffey & C Hunka & J A Sharp & Z Zeng, 2009. "Estimating advertisers' values for paid search clickthroughs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 411-418, March.
    6. Martín Rodríguez, Gloria & Cáceres Hernández, José Juan, 2010. "Splines and the proportion of the seasonal period as a season index," Economic Modelling, Elsevier, vol. 27(1), pages 83-88, January.
    7. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    8. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    9. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
    10. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.

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