References
- Aneiros, G., Cao, R., Vilar-Fernández, J. M., and Muñoz-San-Roque, A. (2011), “Functional Prediction for the Residual Demand in Electricity Spot Markets,” in Recent Advances in Functional Data Analysis and Related Topics, ed. F. Ferraty, New York: Springer, pp. 9--15.
- Antoch, J., Prchal, L., De Rosa, M. R., and Sarda, P. (2008), “Functional Linear Regression With Functional Response: Application to Prediction of Electricity Consumption,” in Functional and Operatorial Statistics, eds. S. Dabo-Niang and F. Ferraty, New York: Springer, pp. 23–29.
- Aue, A., Norinho, D. D., and Hörmann, S. (2015), “On the Prediction of Stationary Functional Time Series,” Journal of the American Statistical Association, 110, 378–392.
- Bellini, F., Klar, B., Müller, A., and Rosazza-Gianin, E. (2014), “Generalized Quantiles as Risk Measures,” Insurance: Mathematics and Economics, 54, 41–48.
- Bremnes, J. B. (2004), “Probabilistic Wind Power Forecasts Using Local Quantile Regression,” Wind Energy, 7, 47–54.
- Cho, H., Goude, Y., Brossat, X., and Yao, Q. (2013), “Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach,” Journal of the American Statistical Association, 108, 7–21.
- Cottet, R., and Smith, M. (2003), “Bayesian Modeling and Forecasting of Intraday Electricity Load,” Journal of the American Statistical Association, 98, 839–849.
- Engel, R. F., Granger, C. W. J., Rice, J., and Weiss, A. (1986), “Semiparametric Estimated of the Relation Between Weather and Electricity sales,” Journal of the American Statistical Association, 81, 310–320.
- EPEX Spot. (2014), “EPEX Spot Annual Report 2013,” available at https://www.epexspot.com/document/28679/EPEX02013.pdf
- Eurostat. (2014), “Supply, Transformation, Consumption - Electricity - Annual Data,” available at http://epp.eurostat.ec.europa.eu
- Ferraty, F., Goia, A., Salinelli, E., and Vieu, P. (2014), “Peak-Load Forecasting Using a Functional Semi-Parametric Approach,” in Topics in Nonparametric Statistics, eds. M. G. Akritas, S. N. Lahiri, D. N. Politis, New York: Springer, pp. 105–114.
- Ferraty, F., and Vieu, P. (2006), Nonparametric Functional Data Analysis: Theory and Practice, New York: Springer.
- Gneiting, T. (2011), “Quantiles as Optimal Point Forecasts,” International Journal of Forecasting, 27, 197–207.
- Goia, A., May, C., and Fusai, G. (2010), “Functional Clustering and Linear Regression for Peak Load Forecasting,” International Journal of Forecasting, 26, 700–711.
- Guler, K., Ng, P. T., and Xiao, Z. (2014), “Mincer-Zarnovitz Quantile and Expectile Regressions for Forecast Evaluations under Asymmetric Loss Functions,” Working Paper Series No. 14-01, Northern Arizona University.
- Guo, M., Zhou, L., Härdle, W. K., and Huang, J. Z. (2015), “Functional Data Analysis of Generalized Regression Quantiles,” Statistics and Computing, 25, 189–202.
- Harvey, A., and Koopman, S. J. (1993), “Forecasting Hourly Electricity Demand Using Time-Varying Splines,” Journal of the American Statistical Association, 88, 1228–1236.
- Hörmann, S., and Kokoszka, P. (2010), “Weakly Dependent Functional Data,” The Annals of Statistics, 38, 1845–1884.
- Horváth, L., and Kokoszka, P. (2012), Inference for Functional Data with Applications, New York: Springer.
- Hosking, J. R. (1980), “The Multivariate Portmanteau Statistic,” Journal of the American Statistical Association, 75, 602–608.
- Hyndman, R. J., and Fan, S. (2010), “Density Forecasting for Long-Term Peak Electricity Demand,” IEEE Transactions on Power Systems, 25, 1142–1153.
- Jones, M. C. (1994), “Expectiles and M-Quantiles are Quantiles,” Statistics & Probability Letters, 20, 149–153.
- Koenker, R. (2005), Quantile Regression, Cambridge, UK: Cambridge University Press.
- Koenker, R., and Bassett Jr, G. (1978), “Regression Quantiles,” Econometrica: Journal of the Econometric Society, 46, 33–50.
- Leutbecher, M., and Palmer, T. (2008), “Ensemble Forecasting,” Journal of Computational Physics, 227, 3515–3539.
- Liebl, D. (2013), “Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective,” The Annals of Applied Statistics, 7, 1562–1592.
- Linnet, U. (2005), “Tools Supporting Wind Energy Trade in Deregulated Markets,” Ph.D. dissertation, Technical University of Denmark.
- Lütkepohl, H. (2005), New Introduction to Multiple Time Series Analysis, New York: Springer.
- Mayer, J. (2014), “Electricity Production and Spot-Prices in Germany 2014,” available at https://www.ise.fraunhofer.de/en/downloads-englisch/pdf-files-englisch/data-nivc-/electricity-spot-prices-and-produ-ction-data-in-germany-2014.pdf
- Newey, W. K., and Powell, J. L. (1987), “Asymmetric Least Squares Estimation and Testing,” Econometrica: Journal of the Econometric Society, 55, 819–847.
- Pinson, P., Chevallier, C., and Kariniotakis, G. N. (2007), “Trading Wind Generation from Short-Term Probabilistic Forecasts of Wind Power,” IEEE Transactions on Power Systems, 22, 1148–1156.
- Ramsay, J. O., and Silverman, B. W. (2002), Applied Functional Data Analysis: Methods and Case Studies, New York: Springer.
- ——— (2005), Functional Data Analysis, New York: Springer.
- Schnabel, S. (2011), “Expectile Smoothing: New Perspectives on Asymmetric Least Squares. An Application to Life Expectancy,” Ph.D. dissertation, Utrecht University.
- Schnabel, S. K., and Eilers, P. H. C. (2013), “Simultaneous Estimation of Quantile Curves using Quantile Sheets,” AStA Advances in Statistical Analysis, 97, 77–87.
- Shang, H. L. (2013), “Functional Time Series Approach for Forecasting Very Short-Term Electricity Demand,” Journal of Applied Statistics, 40, 152–168.
- ——— (2014), “A Survey of Functional Principal Component Analysis,” AStA Advances in Statistical Analysis, 98, 121–142.
- Tay, A. S., and Wallis, K. F. (2000), “Density Forecasting: A Survey,” Journal of Forecasting, 19, 235–254.
- Taylor, J. W. (2008), “Estimating Value at Risk and Expected Shortfall Using Expectiles,” Journal of Financial Econometrics, 6, 231–252.
- ——— (2010), “Triple Seasonal Methods for Short-Term Electricity Demand Forecasting,” European Journal of Operational Research, 204, 139–152.
- Taylor, J. W., and Buizza, R. (2002), “Neural Network Load Forecasting with Weather Ensemble Predictions,” IEEE Transactions on Power Systems, 17, 626–632.
- Taylor, J. W., and McSharry, P. E. (2007), “Short-Term Load Forecasting Methods: An Evaluation Based on European Data,” IEEE Transactions on Power Systems, 22, 2213–2219.
- Tran, N. M., Osipenko, M., and Härdle, W. K. (2016), “Principal Component Analysis in an Asymmetric Norm,” CRC 649 Discussion Paper 2016-040, Humboldt University, Berlin.
- Vilar, J. M., Cao, R., and Aneiros, G. (2012), “Forecasting Next-Day Electricity Demand and Price using Nonparametric Functional Methods,” International Journal of Electrical Power & Energy Systems, 39, 48–55.
- Weron, R. (2007), Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach, New York: Wiley.