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Functional hourly forecasting of water temperature

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  • Mestekemper, Thomas
  • Windmann, Michael
  • Kauermann, Göran
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    Abstract

    The paper describes the problem of forecasting water temperatures on an hourly basis using previous water and air temperatures as predictors. Both time series are decomposed using functional principal components, leading to low dimensional vector autoregressive modeling. The principal component scores mirror serial correlation, which is also incorporated in the model. The modeling exercise is motivated by and demonstrated with data collected in the German river Wupper, and the approach is contrasted to alternative routines which have been suggested in statistics and hydrology.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 26 (2010)
    Issue (Month): 4 (October)
    Pages: 684-699

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    Handle: RePEc:eee:intfor:v:26:y::i:4:p:684-699

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    Web page: http://www.elsevier.com/locate/ijforecast

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    1. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    2. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
    3. Bircan Erbas & Rob J. Hyndman & Dorota M. Gertig, 2005. "Forecasting age-specific breast cancer mortality using functional data models," Monash Econometrics and Business Statistics Working Papers 3/05, Monash University, Department of Econometrics and Business Statistics.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, April.
    5. Cottet R. & Smith M., 2003. "Bayesian Modeling and Forecasting of Intraday Electricity Load," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 839-849, January.
    6. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    7. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, April.
    8. Heij, Christiaan & Groenen, Patrick J.F. & van Dijk, Dick, 2007. "Forecast comparison of principal component regression and principal covariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3612-3625, April.
    9. Cornillon, P.-A. & Imam, W. & Matzner-Lober, E., 2008. "Forecasting time series using principal component analysis with respect to instrumental variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1269-1280, January.
    10. Liu, Dandan & Jansen, Dennis W., 2007. "Macroeconomic forecasting using structural factor analysis," International Journal of Forecasting, Elsevier, vol. 23(4), pages 655-677.
    11. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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