Functional hourly forecasting of water temperature
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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- 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.
- 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.
- Heij, C. & Groenen, P.J.F. & van Dijk, D.J.C., 2005.
"Forecast comparison of principal component regression and principal covariate regression,"
Econometric Institute Research Papers
EI 2005-28, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- 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.
- Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
- Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
- Rob J. Hyndman & Md. Shahid Ullah, 2005.
"Robust forecasting of mortality and fertility rates: a functional data approach,"
Monash Econometrics and Business Statistics Working Papers
2/05, Monash University, Department of Econometrics and Business Statistics.
- 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.
- 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.
- Szymon Borak & Wolfgang Härdle & Enno Mammen & Byeong U. Park, 2007.
"Time Series Modelling with Semiparametric Factor Dynamics,"
SFB 649 Discussion Papers
SFB649DP2007-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Park, Byeong U. & Mammen, Enno & HÃ¤rdle, Wolfgang & Borak, Szymon, 2009. "Time Series Modelling With Semiparametric Factor Dynamics," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 284-298.
- Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, October.
- 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.
- 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.
- Liu, Dandan & Jansen, Dennis W., 2007. "Macroeconomic forecasting using structural factor analysis," International Journal of Forecasting, Elsevier, vol. 23(4), pages 655-677.
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