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1. Alfares, HK and M Nazeeruddin (2002). Electric load forecasting: Literature survey and classification of methods. International journal of systems science 33(1), 23–34.
   Paper not yet in RePEc: Add citation now
   
2. Bacher, P, H Madsen, and HA Nielsen (2009). Online short-term solar power forecasting. Solar Energy 83(10), 1772–1783.
   Paper not yet in RePEc: Add citation now
   

3. Bühlmann, P and B Yu (2003). Boosting With the L2 Loss: Regression and Classification. Journal of the American Statistical Association 98(462), 324–339.

4. Beckel, C, L Sadamori, T Staake, and S Santini (2014). Revealing household characteristics from smart meter data. Energy 78, 397–410.

5. Ben Taieb, Huser, Hyndman & Genton: 9 June 2015 Probabilistic forecasting with boosted additive models: an application to smart meter data Arora, S and JW Taylor (2014). Forecasting Electricity Smart Meter Data Using Conditional Kernel Density Estimation. Omega.
   Paper not yet in RePEc: Add citation now
   

6. Ben Taieb, Huser, Hyndman & Genton: 9 June 2015 Probabilistic forecasting with boosted additive models: an application to smart meter data Gneiting, T, F Balabdaoui, and AE Raftery (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society. Series B, Statistical methodology 69(2), 243– 268.

7. Ben Taieb, Huser, Hyndman & Genton: 9 June 2015 Probabilistic forecasting with boosted additive models: an application to smart meter data Mayr, A, T Hothorn, and N Fenske (2012). Prediction intervals for future BMI values of individual children: a non-parametric approach by quantile boosting. BMC Medical Research Methodology 12, 6.
   Paper not yet in RePEc: Add citation now
   
8. Ben Taieb, Huser, Hyndman & Genton: 9 June 2015 Probabilistic forecasting with boosted additive models: an application to smart meter data Zhu, X and MG Genton (2012). Short-Term Wind Speed Forecasting for Power System Operations. International Statistical Review 80(1), 2–23.
   Paper not yet in RePEc: Add citation now
   

9. Ben Taieb, S and RJ Hyndman (2014). A gradient boosting approach to the Kaggle load forecasting competition. International Journal of Forecasting 30(2), 382–394.

10. Chen, LH, MY Cheng, and L Peng (2009). Conditional variance estimation in heteroscedastic regression models. Journal of Statistical Planning and Inference 139(2), 236–245.
    Paper not yet in RePEc: Add citation now
    

11. Chernozhukov, V, I Fernández-Val, and A Galichon (2010). Quantile and probability curves without crossing. Econometrica: Journal of the Econometric Society 78(3), 1093–1125.

12. Cho, H, Y Goude, X Brossat, and Q Yao (2013). Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach. Journal of the American Statistical Association 108(501), 7–21.

13. Commission For Energy Regulation (2011). Electricity smart metering customer behaviour trials findings report. Tech. rep. Dublin: Commission for Energy Regulation.
    Paper not yet in RePEc: Add citation now
    
14. Engle, RF, CWJ Granger, R Ramanathan, and F Vahid-Araghi (1993). Probabilistic Methods in Forecasting Hourly Loads. Tech. rep. TR-101902. Electric Power Research Institute. http: //www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=TR-101902.
    Paper not yet in RePEc: Add citation now
    

15. Fan, J and Q Yao (1998). Efficient estimation of conditional variance functions in stochastic regression. Biometrika 85(3), 645–660.

16. Fan, S and RJ Hyndman (2012). Short-term load forecasting based on a semi-parametric additive model. IEEE Transactions on Power Systems 27(1), 134–141.

17. Friedman, J and T Hastie (2000). Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Annals of Statistics 28(2), 337–407.
    Paper not yet in RePEc: Add citation now
    
18. Friedman, JH (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics 29(5), 1189–1232.
    Paper not yet in RePEc: Add citation now
    

19. Gneiting, T (2011). Quantiles as optimal point forecasts. International Journal of forecasting 27(2), 197–207.

20. Gneiting, T and AE Raftery (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association 102(477), 359–378.

21. Gneiting, T and M Katzfuss (2014). Probabilistic Forecasting. Annual Review of Statistics and Its Application 1(1), 125–151.
    Paper not yet in RePEc: Add citation now
    

22. Groen, JJJ, R Paap, and F Ravazzolo (2013). Real-Time Inflation Forecasting in a Changing World. Journal of business & economic statistics: a publication of the American Statistical Association 31(1), 29–44.

23. Hastie, TJ, R Tibshirani, and JH Friedman (2008). The elements of statistical learning. Vol. 18. Springer-Verlag, p. 764.
    Paper not yet in RePEc: Add citation now
    
24. Hastie, TJJ and RJJ Tibshirani (1990). Generalized additive models. Chapman & Hall/CRC.
    Paper not yet in RePEc: Add citation now
    
25. Hippert, HS, CE Pedreira, and RC Souza (2001). Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems 16(1), 44–55.
    Paper not yet in RePEc: Add citation now
    
26. Hong, T (2010). “Short Term Electric Load Forecasting”. PhD thesis.
    Paper not yet in RePEc: Add citation now
    
27. Hothorn, T, P Bühlmann, T Kneib, M Schmid, and B Hofner (2010). Model-based boosting 2.0. Journal of Machine Learning Research: JMLR 11, 2109–2113.
    Paper not yet in RePEc: Add citation now
    
28. Hyndman, RJ and G Athanasopoulos (2015). Forecasting: principles and practice. Melbourne, Australia: OTexts.
    Paper not yet in RePEc: Add citation now
    

29. Jones, HE and DJ Spiegelhalter (2012). Improved probabilistic prediction of healthcare performance indicators using bidirectional smoothing models. Journal of the Royal Statistical Society. Series A 175(3), 729–747.

30. Kneib, T (2013). Beyond mean regression. Statistical Modelling 13(4), 275–303.
    Paper not yet in RePEc: Add citation now
    

31. Kneib, T, T Hothorn, and G Tutz (2009). Variable Selection and Model Choice in Geoadditive Regression Models. Biometrics 65(003), 626–634.

32. Koenker, R (2005). Quantile Regression. Econometric Society Monographs. Cambridge University Press.

33. Lou, Y, R Caruana, J Gehrke, and G Hooker (2013). Accurate intelligible models with pairwise interactions. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp.623–631.
    Paper not yet in RePEc: Add citation now
    
34. Palmer, TN (2012). Towards the probabilistic Earth-system simulator: a vision for the future of climate and weather prediction. Quarterly Journal of the Royal Meteorological Society 138(665), 841–861.
    Paper not yet in RePEc: Add citation now
    
35. Pompey, P, A Bondu, Y Goude, and M Sinn (2014). “Massive-Scale Simulation of Electrical Load in Smart Grids using Generalized Additive Models”. In: Lecture Notes in Statistics: Modeling and Stochastic Learning for Forecasting in High Dimension. Springer.
    Paper not yet in RePEc: Add citation now
    
36. Ruppert, D (2002). Selecting the Number of Knots for Penalized Splines. Journal of Computational and Graphical Statistics 11, 735–757.
    Paper not yet in RePEc: Add citation now
    
37. Schapire, RE (1990). The strength of weak learnability. Machine Learning 5(2), 197–227.
    Paper not yet in RePEc: Add citation now
    
38. Schapire, RE and Y Freund (2012). Boosting: Foundations and Algorithms. The MIT Press.
    Paper not yet in RePEc: Add citation now
    

39. Schmid, M and T Hothorn (2008). Boosting Additive Models using component-wise P-Splines. Computational Statistics & Data Analysis 53(002), 298–311.

40. Sevlian, R and R Rajagopal (2013). Value of aggregation in smart grids. In: Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference on, pp.714–719.
    Paper not yet in RePEc: Add citation now
    
41. Sevlian, R and R Rajagopal (2014). A Model For The Effect of Aggregation on Short Term Load Forecasting. In: IEEE Power and Energy Society General Meeting.
    Paper not yet in RePEc: Add citation now
    
42. Sevlian, R, S Patel, and R Rajagopal (2014). Distribution System Load and Forecast Model. arXiv: 1407.3322 [stat.AP].
    Paper not yet in RePEc: Add citation now
    
43. Spiegelhalter, DJ (2014). Statistics. The future lies in uncertainty. Science 345(6194), 264–265.
    Paper not yet in RePEc: Add citation now
    
44. Tao, H and S Fan (2014). “Probabilistic Electric Load Forecasting: A Tutorial Review”. Submitted to International Journal of Forecasting.
    Paper not yet in RePEc: Add citation now
    

45. Tashman, LJ (2000). Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting 16(4), 437–450.

46. Taylor, JW (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research 204(1), 139–152.

47. Wijaya, TK, M Sinn, and B Chen (2015). Forecasting Uncertainty in Electricity Demand. In: AAAI-15 Workshop on Computational Sustainability.
    Paper not yet in RePEc: Add citation now
    
48. Zheng, J, DW Gao, and L Lin (2013). Smart Meters in Smart Grid: An Overview. In: Green Technologies Conference, 2013 IEEE, pp.57–64.
    Paper not yet in RePEc: Add citation now
    

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    In: Working Paper Series of the Department of Economics, University of Konstanz.
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    In: International Journal of Forecasting.
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    In: International Journal of Forecasting.
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    In: Journal of Econometrics.
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    In: Statistical Applications in Genetics and Molecular Biology.
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    In: Advances in Data Analysis and Classification.
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    In: Handbook of Economic Forecasting.
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    In: Economics Letters.
    RePEc:eee:ecolet:v:113:y:2011:i:1:p:16-18.

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    In: Computational Statistics & Data Analysis.
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    In: Annals of the Institute of Statistical Mathematics.
    RePEc:spr:aistmt:v:62:y:2010:i:2:p:235-248.

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    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:54:y:2010:i:10:p:2203-2213.

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    In: Statistical Applications in Genetics and Molecular Biology.
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    In: Journal of Applied Econometrics.
    RePEc:jae:japmet:v:24:y:2009:i:4:p:607-629.

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    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:53:y:2009:i:7:p:2453-2464.

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    In: Computational Statistics & Data Analysis.
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    In: ifo Schnelldienst.
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    Full description at Econpapers || Download paper

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    In: Journal of Time Series Econometrics.
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    Full description at Econpapers || Download paper

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    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:53:y:2008:i:2:p:298-311.

    Full description at Econpapers || Download paper

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    In: Statistical Applications in Genetics and Molecular Biology.
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    Full description at Econpapers || Download paper

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    In: Technical Reports.
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    Full description at Econpapers || Download paper

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    In: University of St. Gallen Department of Economics working paper series 2007.
    RePEc:usg:dp2007:2007-24.

    Full description at Econpapers || Download paper

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    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:52:y:2007:i:1:p:325-334.

    Full description at Econpapers || Download paper

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    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:51:y:2007:i:9:p:4605-4621.

    Full description at Econpapers || Download paper

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    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:51:y:2007:i:12:p:6044-6059.

    Full description at Econpapers || Download paper

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    In: Computational Statistics & Data Analysis.
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    Full description at Econpapers || Download paper

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    In: Computing in Economics and Finance 2005.
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    Full description at Econpapers || Download paper

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    In: Statistics & Probability Letters.
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    Full description at Econpapers || Download paper

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