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[1] Dmitry Yarotsky,et al. Error bounds for approximations with deep ReLU networks , 2016, Neural Networks.
[2] Nicholas G. Polson,et al. Deep learning for spatio‐temporal modeling: Dynamic traffic flows and high frequency trading , 2017, Applied Stochastic Models in Business and Industry.
[3] Hassan Soltan,et al. A methodology for Electric Power Load Forecasting , 2011 .
[4] Richard L. Smith. Risk Management: Measuring Risk with Extreme Value Theory , 2002 .
[5] Zijun Zhang,et al. Short-Term Electricity Price Forecasting With Stacked Denoising Autoencoders , 2017, IEEE Transactions on Power Systems.
[6] Kenneth A. Lindsay,et al. Forecasting spikes in electricity prices , 2012 .
[7] Johannes Schmidt-Hieber,et al. Nonparametric regression using deep neural networks with ReLU activation function , 2017, The Annals of Statistics.
[8] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[9] Nicole A. Lazar,et al. Statistics of Extremes: Theory and Applications , 2005, Technometrics.
[10] Ohad Shamir,et al. The Power of Depth for Feedforward Neural Networks , 2015, COLT.
[11] P. McSharry,et al. Short-Term Load Forecasting Methods: An Evaluation Based on European Data , 2007, IEEE Transactions on Power Systems.
[12] F. Benth,et al. Pricing Futures and Options in Electricity Markets , 2014 .
[13] Lorenzo Rosasco,et al. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review , 2016, International Journal of Automation and Computing.
[14] J. Tawn,et al. Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications , 2004 .
[15] T. Senjyu,et al. A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method , 2007, IEEE Transactions on Power Systems.
[16] S. Coles,et al. An Introduction to Statistical Modeling of Extreme Values , 2001 .
[17] Ser-Huang Poon,et al. Hedging the Black Swan: Conditional Heteroskedasticity and Tail Dependence in S&P500 and Vix , 2009 .
[18] Stuart A. Klugman,et al. On the estimation of long tailed skewed distributions with actuarial applications , 1983 .
[19] Nicholas G. Polson,et al. Deep learning for short-term traffic flow prediction , 2016, 1604.04527.
[20] Y.-y. Hong,et al. Locational marginal price forecasting in deregulated electricity markets using artificial intelligence , 2002 .
[21] Pravin Varaiya,et al. Smart Operation of Smart Grid: Risk-Limiting Dispatch , 2011, Proceedings of the IEEE.
[22] Marco van Akkeren,et al. A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.
[23] Wojciech Czarnecki,et al. On Loss Functions for Deep Neural Networks in Classification , 2017, ArXiv.
[24] Thorsten Rheinländer. Risk Management: Value at Risk and Beyond , 2003 .
[25] Caston Sigauke,et al. Extreme daily increases in peak electricity demand: Tail-quantile estimation , 2013 .
[26] M. Genton,et al. Powering Up With Space-Time Wind Forecasting , 2010 .
[27] V. Mendes,et al. Short-term electricity prices forecasting in a competitive market: A neural network approach , 2007 .
[28] Derek W. Bunn. Short-Term Forecasting: A Review of Procedures in the Electricity Supply Industry , 1982 .
[29] Philippe Naveau,et al. Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection , 2016 .
[30] Matus Telgarsky,et al. Neural Networks and Rational Functions , 2017, ICML.
[31] Jürgen Schmidhuber,et al. LSTM can Solve Hard Long Time Lag Problems , 1996, NIPS.
[32] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[33] C. Hsiao,et al. Locational marginal price forecasting in deregulated electric markets using a recurrent neural network , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).
[34] D. Kirschen,et al. Forecasting system imbalance volumes in competitive electricity markets , 2004, IEEE Transactions on Power Systems.
[35] Francis R. Bach,et al. Breaking the Curse of Dimensionality with Convex Neural Networks , 2014, J. Mach. Learn. Res..
[36] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[37] Paul Embrechts,et al. Infinite-mean models and the LDA for operational risk , 2006 .
[38] D. Dupuis. Electricity price dependence in New York State zones: A robust detrended correlation approach , 2017 .
[39] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[40] Saahil Shenoy,et al. Risk adjusted forecasting of electric power load , 2014, 2014 American Control Conference.
[41] Mun-Kyeom Kim,et al. A New Approach to Short-term Price Forecast Strategy with an Artificial Neural Network Approach: Application to the Nord Pool , 2015 .
[42] Francis R. Bach,et al. Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression , 2016, J. Mach. Learn. Res..
[43] Pedro Emanuel Almeida Cardoso,et al. Deep Learning Applied to PMU Data in Power Systems , 2017 .
[44] Michael Goldstein,et al. Quantifying uncertainty in wholesale electricity price projections using Bayesian emulation of a generation investment model , 2018 .
[45] Remy Cottet,et al. Bayesian Modeling and Forecasting of Intraday Electricity Load , 2003 .
[46] B. Gnedenko. Sur La Distribution Limite Du Terme Maximum D'Une Serie Aleatoire , 1943 .
[47] Vadim Sokolov,et al. Deep Learning: A Bayesian Perspective , 2017, ArXiv.
[48] Dominik Liebl,et al. Modeling and forecasting electricity spot prices: A functional data perspective , 2013, 1310.1628.
[49] Wei-Peng Chen,et al. Model selection criteria for short-term microgrid-scale electricity load forecasts , 2013, 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT).
[50] Eric P. Smith,et al. An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.
[51] A. Davison,et al. Statistical Modeling of Spatial Extremes , 2012, 1208.3378.
[52] Richard L. Smith. Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone , 1989 .
[53] Chen-Ching Liu,et al. Day-Ahead Electricity Price Forecasting in a Grid Environment , 2007, IEEE Transactions on Power Systems.
[54] M. Makarov. Extreme value theory and high quantile convergence , 2006 .
[55] Richard L. Smith,et al. Models for exceedances over high thresholds , 1990 .
[56] H. Madsen,et al. Forecasting Electricity Spot Prices Accounting for Wind Power Predictions , 2013, IEEE Transactions on Sustainable Energy.
[57] Nicholas G. Polson,et al. Bayesian analysis of traffic flow on interstate I-55: The LWR model , 2014, 1409.6034.