Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications
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Saeid Nahavandi | Abbas Khosravi | Mohammad Anwar Hosen | H. M. Dipu Kabir | S. Nahavandi | A. Khosravi | M. Hosen | H. M. D. Kabir
[1] Mansun Chan,et al. Modeling of fringe current for semiconductor-extended organic TFTs , 2016, 2016 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC).
[2] Amir F. Atiya,et al. Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances , 2011, IEEE Transactions on Neural Networks.
[3] Vania Almeida,et al. Prediction intervals for electric load forecast: Evaluation for different profiles , 2015, 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP).
[4] Mukta Paliwal,et al. Neural networks and statistical techniques: A review of applications , 2009, Expert Syst. Appl..
[5] Nadarajah Mithulananthan,et al. PV based EV charging at universities using supplied historical PV output ramp , 2018 .
[6] Liping Xie,et al. Direct interval forecasting of wind speed using radial basis function neural networks in a multi-objective optimization framework , 2016, Neurocomputing.
[7] Irena Koprinska,et al. Forecasting hourly electricity load profile using neural networks , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).
[8] Zongxiang Lu,et al. Probabilistic short-term wind power forecasting based on deep neural networks , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).
[9] Saeid Nahavandi,et al. A prediction interval-based approach to determine optimal structures of neural network metamodels , 2010, Expert Syst. Appl..
[10] Kwok-Wing Chau,et al. ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS , 2015, Eng. Appl. Artif. Intell..
[11] John Bjørnar Bremnes,et al. Probabilistic wind power forecasts using local quantile regression , 2004 .
[12] Carlos F.M. Coimbra,et al. Real-time prediction intervals for intra-hour DNI forecasts , 2015 .
[13] Inés María Galván,et al. Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks , 2017, Inf. Sci..
[14] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[15] Paras Mandal,et al. A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting , 2014, IEEE Transactions on Power Systems.
[16] Tao Lu,et al. Prediction of indoor temperature and relative humidity using neural network models: model comparison , 2009, Neural Computing and Applications.
[17] W. Rivera,et al. Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks , 2009 .
[18] Saeid Nahavandi,et al. Closure to the Discussion of “Prediction Intervals for Short-Term Wind Farm Generation Forecasts” and “Combined Nonparametric Prediction Intervals for Wind Power Generation” and the Discussion of “Combined Nonparametric Prediction Intervals for Wind Power Generation” , 2014 .
[19] Saeid Nahavandi,et al. An optimized mean variance estimation method for uncertainty quantification of wind power forecasts , 2014 .
[20] Michael Negnevitsky,et al. Wind speed forecast model for wind farm based on a hybrid machine learning algorithm , 2015 .
[21] Pedro Vit,et al. Interval prediction for a Poisson process , 1973 .
[22] Aidong Adam Ding,et al. Backpropagation of pseudo-errors: neural networks that are adaptive to heterogeneous noise , 2003, IEEE Trans. Neural Networks.
[23] Felipe Valencia,et al. Prediction interval based on type-2 fuzzy systems for wind power generation and loads in microgrid control design , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[24] Saeid Nahavandi,et al. Construction of Optimal Prediction Intervals for Load Forecasting Problems , 2010, IEEE Transactions on Power Systems.
[25] Zhenhuan Gong,et al. PRESS: PRedictive Elastic ReSource Scaling for cloud systems , 2010, 2010 International Conference on Network and Service Management.
[26] Peter Auer,et al. Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..
[27] Hana Ahmad Sartawi,et al. Bayesian prediction bounds for the burr type X model , 1991 .
[28] A. Weigend,et al. Estimating the mean and variance of the target probability distribution , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).
[29] Kit Po Wong,et al. Optimal Prediction Intervals of Wind Power Generation , 2014, IEEE Transactions on Power Systems.
[30] R. Stine. Bootstrap Prediction Intervals for Regression , 1985 .
[31] Yonghua Song,et al. Nonparametric Prediction Intervals of Wind Power via Linear Programming , 2018, IEEE Transactions on Power Systems.
[32] Francesco Giordano,et al. Forecasting nonlinear time series with neural network sieve bootstrap , 2007, Comput. Stat. Data Anal..
[33] Kashem M. Muttaqi,et al. Sustainable energy system design with distributed renewable resources considering economic, environmental and uncertainty aspects , 2015 .
[34] Marc Peter Deisenroth,et al. Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.
[35] Saeid Nahavandi,et al. Prediction interval-based neural network modelling of polystyrene polymerization reactor – A new perspective of data-based modelling , 2014 .
[36] Padraig Cunningham,et al. Confidence and prediction intervals for neural network ensembles , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).
[37] John P. Parmigiani,et al. An experimental assessment of methods to predict crack deflection at an interface , 2017 .
[38] Xun Gong,et al. Bootstrap prediction interval estimation for wind speed forecasting , 2015, 2015 IEEE Energy Conversion Congress and Exposition (ECCE).
[39] J. Richard Landis. Introductory statistical analysis , 1974 .
[40] Abbas Khosravi,et al. Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[41] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[42] Saeid Nahavandi,et al. Constructing Optimal Prediction Intervals by Using Neural Networks and Bootstrap Method , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[43] Carlos E. Pedreira,et al. Neural networks for short-term load forecasting: a review and evaluation , 2001 .
[44] Saeid Nahavandi,et al. Quantifying uncertainties of neural network-based electricity price forecasts , 2013 .
[45] Tomonobu Senjyu,et al. A new strategy to quantify uncertainties of wavelet-GRNN-PSO based solar PV power forecasts using bootstrap confidence intervals , 2015, 2015 IEEE Power & Energy Society General Meeting.
[46] Raymond K. Wong,et al. Currency Exchange Rate Forecasting From News Headlines , 2002, Australasian Database Conference.
[47] Saeid Nahavandi,et al. Improving the Quality of Prediction Intervals Through Optimal Aggregation , 2015, IEEE Transactions on Industrial Electronics.
[48] Ronald J. Wonnacott,et al. Trade and Investment in a Hub‐and‐Spoke System Versus a Free Trade Area , 1996 .
[49] H. J. Van Zuylen,et al. Bayesian committee of neural networks to predict travel times with confidence intervals , 2009 .
[50] Jakub Nowotarski,et al. LTSCWAVE: MATLAB function to estimate and forecast the long-term seasonal component (LTSC) of an electricity spot price series using wavelet-based methods , 2013 .
[51] Robert Tibshirani,et al. A Comparison of Some Error Estimates for Neural Network Models , 1996, Neural Computation.
[52] J. Robinson,et al. Sequential choice of an optimal dose: A prediction intervals approach , 1978 .
[53] M. Elena Martinez-Perez,et al. Retinal degeneration in children: Dark adapted visual threshold and arteriolar diameter , 2008, Vision Research.
[54] Mohammad Anwar Hosen. Prediction interval-based modelling and control of nonlinear processes , 2015 .
[55] Luren Yang,et al. An Evaluation of Confidence Bound Estimation Methods for Neural Networks , 2002, Advances in Computational Intelligence and Learning.
[56] Keith Worden,et al. Uncertainty analysis of a neural network used for fatigue lifetime prediction , 2008 .
[57] Simon J. Mason,et al. Comparison of Some Statistical Methods of Probabilistic Forecasting of ENSO. , 2002 .
[58] Nadarajah Mithulananthan,et al. A Day-Ahead Forecasting Model for Probabilistic EV Charging Loads at Business Premises , 2018, IEEE Transactions on Sustainable Energy.
[59] Yanqing Zhang,et al. Statistical fuzzy interval neural networks for currency exchange rate time series prediction , 2007, Appl. Soft Comput..
[60] Chris Chatfield,et al. Prediction Intervals for Time-Series Forecasting , 2001 .
[61] Hussain Mohammed Dipu Kabir,et al. A Resilient Auction Framework for Deadline-Aware Jobs in Cloud Spot Market , 2017, 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS).
[62] Paras Mandal,et al. Solar PV power generation forecast using a hybrid intelligent approach , 2013, 2013 IEEE Power & Energy Society General Meeting.
[63] Chris Chatfield,et al. Calculating Interval Forecasts , 1993 .
[64] Farouq S Mjalli,et al. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. , 2007, Journal of environmental management.
[65] Jianxue Wang,et al. Review on probabilistic forecasting of wind power generation , 2014 .
[66] K. P. Sudheer,et al. Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models , 2016, Modeling Earth Systems and Environment.
[67] Bijaya K. Panigrahi,et al. Prediction interval estimation for electricity price and demand using support vector machines , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).
[68] Ashok N. Srivistava,et al. Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection , 2012 .
[69] Pierre Pinson,et al. Discussion of “Prediction Intervals for Short-Term Wind Farm Generation Forecasts” and “Combined Nonparametric Prediction Intervals for Wind Power Generation” , 2014 .
[70] Michael P. Clements,et al. Evaluating interval forecasts of high-frequency financial data , 2003 .
[71] M. Valipour. Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms , 2016 .
[72] R. D. Veaux,et al. Prediction intervals for neural networks via nonlinear regression , 1998 .
[73] Z. Dong,et al. A Statistical Approach for Interval Forecasting of the Electricity Price , 2008, IEEE Transactions on Power Systems.
[74] F. Fisher. Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note , 1970 .
[75] Peter F. Christoffersen. Evaluating Interval Forecasts , 1998 .
[76] David Hinkley,et al. Bootstrap Methods: Another Look at the Jackknife , 2008 .
[77] J. Felsenstein. CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP , 1985, Evolution; international journal of organic evolution.
[78] Bijaya K. Panigrahi,et al. Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines , 2015, IEEE Transactions on Industrial Informatics.
[79] J. T. Hwang,et al. Prediction Intervals for Artificial Neural Networks , 1997 .
[80] Irena Koprinska,et al. Forecasting solar power generated by grid connected PV systems using ensembles of neural networks , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[81] R. Weron. A look into the future of electricity (spot) price forecasting , 2014 .
[82] Saeid Nahavandi,et al. Partial Adversarial Training for Prediction Interval , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).
[83] Tom Schaul,et al. Unifying Count-Based Exploration and Intrinsic Motivation , 2016, NIPS.
[84] Zhao Xu,et al. Direct Interval Forecasting of Wind Power , 2013, IEEE Transactions on Power Systems.
[85] E. Zio,et al. A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes , 2006, IEEE Transactions on Nuclear Science.
[86] Adnan M. Awad,et al. Large sample prediction intervals for a future sample mean: A comparative study , 1986 .
[87] Jakub Nowotarski,et al. Computing electricity spot price prediction intervals using quantile regression and forecast averaging , 2015, Comput. Stat..
[88] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[89] Hiroshi Nishiura. Early Detection of Nosocomial Outbreaks Caused by Rare Pathogens: A Case Study Employing Score Prediction Interval , 2012, Osong public health and research perspectives.
[90] S. Roberts,et al. Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks , 2001 .
[91] Hyun Myung,et al. Cooperative Particle Swarm Optimization-Based Predictive Controller for Multi-robot Formation , 2012, IAS.
[92] Gerald J. Hahn,et al. Factors for Calculating Two-Sided Prediction Intervals for Samples from a Normal Distribution , 1969 .
[93] Zainal Ahmad,et al. Modelling and control of different types of polymerization processes using neural networks technique: A review , 2010 .
[94] Abbas Khosravi. Construction and optimisation of prediction intervals for neural network models , 2010 .
[95] Anuj Abraham,et al. Very Short Term Wind Power Forecasting Using PSO-Neural Network Hybrid System , 2011, ACC.
[96] Abbas Khosravi,et al. Uncertainty handling using neural network-based prediction intervals for electrical load forecasting , 2014 .
[97] George Stavrakakis,et al. Wind power forecasting using advanced neural networks models , 1996 .
[98] Yi-Chung Hu,et al. Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks , 2010, Expert Syst. Appl..
[99] Yonggang Wu,et al. An Advanced Approach for Construction of Optimal Wind Power Prediction Intervals , 2015, IEEE Transactions on Power Systems.
[100] Shen Liu,et al. Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals , 2011 .
[101] Jin Lin,et al. Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation , 2017, IEEE Transactions on Power Systems.
[102] Ruddy Blonbou,et al. Very short-term wind power forecasting with neural networks and adaptive Bayesian learning , 2011 .
[103] Michael P. Clements,et al. Evaluating the Bank of England Density Forecasts of Inflation , 2004 .
[104] Zhang Yan,et al. A review on the forecasting of wind speed and generated power , 2009 .
[105] Saeid Nahavandi,et al. Prediction interval with examples of similar pattern and prediction strength , 2017, 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE).
[106] Saeid Nahavandi,et al. Improving Prediction Interval Quality: A Genetic Algorithm-Based Method Applied to Neural Networks , 2009, ICONIP.
[107] Samuel D. Stearns. Arithmetic coding in lossless waveform compression , 1995, IEEE Trans. Signal Process..
[108] Kit Po Wong,et al. Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.
[109] Kit Po Wong,et al. Discussion of “Combined Nonparametric Prediction Intervals for Wind Power Generation” , 2014 .
[110] Amir F. Atiya,et al. Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals , 2011, IEEE Transactions on Neural Networks.
[111] R. Weron. Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach , 2006 .
[112] Torsten Hothorn,et al. Prediction intervals for future BMI values of individual children - a non-parametric approach by quantile boosting , 2012, BMC Medical Research Methodology.
[113] Pierre Pinson,et al. Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression , 2016, IEEE Transactions on Smart Grid.
[114] D. Heien,et al. The Demand for Dairy Products: Structure, Prediction, and Decomposition , 1988 .
[115] Israel Saguy,et al. STATISTICAL EVALUATION OF ARRHENIUS MODEL AND ITS APPLICABILITY IN PREDICTION OF FOOD QUALITY LOSSES , 1985 .
[116] O. B. Chedzoy,et al. Introductory Statistical Analysis , 1975 .
[117] Thong Ngee Goh,et al. Neural network modeling with confidence bounds: a case study on the solder paste deposition process , 2001 .
[118] Mohd Ali Hashim,et al. Prediction of deep eutectic solvents densities at different temperatures , 2011 .
[119] Kostas S. Metaxiotis,et al. Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .
[120] Javier Arroyo Gallardo,et al. Different approaches to forecast interval time series: a comparison in finance , 2011 .
[121] M Jones,et al. Accuracy of point predictions in survival analysis , 2001, Statistics in medicine.
[122] Jagdish K. Patel,et al. Prediction intervals - a review , 1989 .
[123] Durga L. Shrestha,et al. Machine learning approaches for estimation of prediction interval for the model output , 2006, Neural Networks.
[124] Vincent Mok,et al. Stroke risk assessment for the community by automatic retinal image analysis using fundus photograph , 2016 .
[125] Tom Heskes,et al. Practical Confidence and Prediction Intervals , 1996, NIPS.
[126] Ying Wang,et al. Optimal Wind Power Uncertainty Intervals for Electricity Market Operation , 2018, IEEE Transactions on Sustainable Energy.
[127] P. Pinson,et al. Wind power in electricity markets and the value of forecasting , 2017 .
[128] John P. Parmigiani,et al. Effect of incident angle on crack propagation at interfaces , 2016 .