Forecasting electricity spot prices using time-series models with a double temporal segmentation

References

• Bates, J. M., and C. W. J. Granger. 1969. “The Combination of Forecasts.” Journal of the Operational Research Society 20: 451–468. doi:10.1057/jors.1969.103.
   Web of Science ®Google Scholar
• Bordignon, S., D. W. Bunn, F. Lisi, and F. Nan. 2013. “Combining Day-Ahead Forecasts for British Electricity Prices.” Energy Economics 35: 88–103. doi:10.1016/j.eneco.2011.12.001.
   Web of Science ®Google Scholar
• Busetti, F., and J. Marcucci. 2013. “Comparing Forecast Accuracy: A Monte Carlo Investigation.” International Journal of Forecasting 29: 13–27. doi:10.1016/j.ijforecast.2012.04.011.
   Web of Science ®Google Scholar
• Chen, D., and D. W. Bunn. 2010. “Analysis of the Nonlinear Response of Electricity Prices to Fundamental and Strategic Factors.” IEEE Transactions on Power Systems 25: 595–606. doi:10.1109/TPWRS.2009.2035129.
   Web of Science ®Google Scholar
• Conejo, A. J., J. Contreras, R. Espinola, and M. A. Plazas. 2005. “Forecasting Electricity Prices for a Day-Ahead Pool-Based Electric Energy Market.” International Journal of Forecasting 21: 435–462. doi:10.1016/j.ijforecast.2004.12.005.
   Web of Science ®Google Scholar
• Crespo Cuaresma, J., J. Hlouskova, S. Kossmeier, and M. Obersteiner. 2004. “Forecasting Electricity Spot-Prices Using Linear Univariate Time-Series Models.” Applied Energy 77: 87–106. doi:10.1016/S0306-2619(03)00096-5.
   Web of Science ®Google Scholar
• Crone, S. F., and N. Kourentzes. 2011. “Segmenting Electrical Load Time Series for Forecasting? An Empirical Evaluation of Daily UK Load Patterns.” Paper presented at the 2011 International Joint Conference on Neural Networks, San Jose, CA, July 31–August 5.
   Google Scholar
• Diebold, F. X., and R. S. Mariano. 1995. “Comparing Predictive Accuracy.” Journal of Business & Economic Statistics 13: 253–263.
   Web of Science ®Google Scholar
• Diebold, F. X., R. S. Mariano, J. H. Lee, and G. C. Weinbach. 1994. “Regime Switching with Time Varying Transition Probabilities.” In Nonstationary Time Series Analysis and Cointegration, edited by C. P. Hargreaves, 283–302. Oxford: Oxford University Press.
   Google Scholar
• García-Martos, C., J. Rodríguez, and M. J. Sánchez. 2011. “Forecasting Electricity Prices and Their Volatilities Using Unobserved Components.” Energy Economics 33: 1227–1239. doi:10.1016/j.eneco.2011.07.005.
   Web of Science ®Google Scholar
• Granger, C. W. J., and T. Terasvirta. 1993. Modelling Nonlinear Economic Relationships. Oxford: Oxford University Press.
   Google Scholar
• Hamilton, J. 1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57: 357–384. doi:10.2307/1912559.
   Web of Science ®Google Scholar
• Hansen, P. R., A. Lunde, and J. M. Nason. 2011. “The Model Confidence Set.” Econometrica 79 (2): 453–497. doi:10.3982/ECTA5771.
   Web of Science ®Google Scholar
• Harvey, D., S. Leybourne, and P. Newbold. 1997. “Testing the Equality of Prediction Mean Squared Errors.” International Journal of Forecasting 13: 281–291. doi:10.1016/S0169-2070(96)00719-4.
   Web of Science ®Google Scholar
• Huisman, R., C. Huurman, and R. Mahieu. 2007. “Hourly Electricity Prices in Day-Ahead Markets.” Energy Economics 29: 240–248. doi:10.1016/j.eneco.2006.08.005.
   Web of Science ®Google Scholar
• Kapetanios, G., V. Labhard, and S. Price. 2006. “Forecasting Using Predictive Likelihood Model Averaging.” Economics Letters 91: 373–379. doi:10.1016/j.econlet.2005.12.017.
   Web of Science ®Google Scholar
• Karakatsani, N. V., and D. W. Bunn. 2008. “Forecasting Electricity Prices: The Impact of Fundamentals and Time-Varying Coefficients.” International Journal of Forecasting 24: 764–785. doi:10.1016/j.ijforecast.2008.09.008.
   Web of Science ®Google Scholar
• Kosater, P., and K. Mosler. 2006. “Can Markov Regime-Switching Models Improve Power-Price Forecasts? Evidence from German Daily Power Prices.” Applied Energy 83: 943–958.
   Web of Science ®Google Scholar
• Liu, H., and J. Shi. 2013. “Applying ARMA-GARCH Approaches to Forecasting Short-Term Electricity Prices.” Energy Economics 37: 152–166. doi:10.1016/j.eneco.2013.02.006.
   Web of Science ®Google Scholar
• Misiorek, A., S. Trueck, and R. Weron. 2006. “Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models.” Studies in Nonlinear Dynamics and Econometrics 10: 3.
   Google Scholar
• Newbold, P., and C. W. J. Granger. 1974. “Experience with Forecasting Univariate Time Series and the Combination of Forecasts (With Discussion).” Journal of the Royal Statistical Society. Series A (General) 137: 131–164. doi:10.2307/2344546.
   Web of Science ®Google Scholar
• Nogales, F. J., J. Contreras, A. J. Conejo, and R. Espinola. 2002. “Forecasting Next-Day Electricity Prices by Time Series Models.” IEEE Transactions on Power Systems 17 (2): 342–348. doi:10.1109/TPWRS.2002.1007902.
   Web of Science ®Google Scholar
• Nowotarski, J., E. Raviv, S. Trück, and R. Weron. 2014. “An Empirical Comparison of Alternative Schemes for Combining Electricity Spot Price Forecasts.” Energy Economics 46: 395–412. doi:10.1016/j.eneco.2014.07.014.
   Web of Science ®Google Scholar
• Nowotarski, J., J. Tomczyk, and R. Weron. 2013. “Robust Estimation and Forecasting of the Long-Term Seasonal Component of Electricity Spot Prices.” Energy Economics 39: 13–27. doi:10.1016/j.eneco.2013.04.004.
   Web of Science ®Google Scholar
• Panagiotelis, A., and M. Smith. 2008. “Bayesian Density Forecasting of Intraday Electricity Prices Using Multivariate Skew t distributions.” International Journal of Forecasting 24: 710–727. doi:10.1016/j.ijforecast.2008.08.009.
   Web of Science ®Google Scholar
• Popova, J. 2004. Spatial Pattern in Modelling Electricity Prices: Evidence from the PJM Market. Working Paper, West Virginia University.
   Google Scholar
• Raviv, E., K. E. Bouwman, and D. Van Dijk. 2015. “Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices.” Energy Economics 50: 227–239. doi:10.1016/j.eneco.2015.05.014.
   Web of Science ®Google Scholar
• RTE. 2012. “La Vague De Froid De Février 2012.” Document De La Direction Economie Prospective Et Transparence. http://www.rte-france.com/sites/default/files/rex_vague_froid-2012.pdf
   Google Scholar
• Stock, J. H., and M. W. Watson. 1999. “A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series.” In Cointegration, Causality and Forecasting. A Festschrift in Honour of Clive W.J. Granger, edited by R. F. Engle and H. White. Oxford: Oxford University Press.
   Google Scholar
• Weron, R., and A. Misiorek. 2008. “Forecasting Spot Electricity Prices: A Comparison of Parametric and Semiparametric Time Series Models.” International Journal of Forecasting 24: 744–763. doi:10.1016/j.ijforecast.2008.08.004.
   Web of Science ®Google Scholar
• Yang, Y. 2004. “Combining Forecasting Procedures: Some Theoretical Results.” Econometric Theory 20: 176–222. doi:10.1017/S0266466604201086.
   Web of Science ®Google Scholar
• Zou, H., and Y. Yang. 2004. “Combining Time Series Models for Forecasting.” International Journal of Forecasting 20: 69–84. doi:10.1016/S0169-2070(03)00004-9.
   Web of Science ®Google Scholar