Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes
暂无分享,去创建一个
Demetris Koutsoyiannis | Georgia Papacharalampous | Hristos Tyralis | Demetris Koutsoyiannis | Hristos Tyralis | Georgia Papacharalampous
[1] Jun Guo,et al. Monthly streamflow forecasting based on improved support vector machine model , 2011, Expert Syst. Appl..
[2] C. Sutton. Classification and Regression Trees, Bagging, and Boosting , 2005 .
[3] Ping-Feng Pai,et al. A recurrent support vector regression model in rainfall forecasting , 2007 .
[4] M. Taqqu,et al. Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation , 1997 .
[5] F. Pappenberger,et al. Communicating uncertainty in hydro‐meteorological forecasts: mission impossible? , 2010 .
[6] Juan B. Valdés,et al. NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .
[7] Paresh Chandra Deka,et al. Support vector machine applications in the field of hydrology: A review , 2014, Appl. Soft Comput..
[8] Shie-Yui Liong,et al. Forecasting of hydrologic time series with ridge regression in feature space , 2007 .
[9] Guy G. Gable,et al. Integrating case study and survey research methods: an example in information systems , 1994 .
[10] Lingzhi Wang,et al. A Novel Nonlinear Combination Model Based on Support Vector Machine for Rainfall Prediction , 2011, 2011 Fourth International Joint Conference on Computational Sciences and Optimization.
[11] San Cristóbal Mateo,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996 .
[12] J. Suykens,et al. Time Series Prediction using LS-SVMs , 2008 .
[13] D. G. Watts,et al. Application of Linear Random Models to Four Annual Streamflow Series , 1970 .
[14] P. Coulibaly,et al. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting , 2012 .
[15] Maria-Helena Ramos,et al. How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction , 2015 .
[16] J. P. King,et al. Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River, China , 2010 .
[17] Demetris Koutsoyiannis,et al. HESS Opinions "A random walk on water" , 2009 .
[18] Demetris Koutsoyiannis,et al. Discussion of “Generalized regression neural networks for evapotranspiration modelling” , 2007 .
[19] X. Wen,et al. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region , 2014 .
[20] Ravi Sankar,et al. Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.
[21] Amir F. Atiya,et al. An Empirical Comparison of Machine Learning Models for Time Series Forecasting , 2010 .
[22] Amir F. Atiya,et al. A comparison between neural-network forecasting techniques-case study: river flow forecasting , 1999, IEEE Trans. Neural Networks.
[23] R. Brown. Statistical forecasting for inventory control , 1960 .
[24] Holger R. Maier,et al. Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..
[25] Demetris Koutsoyiannis,et al. Forecasting of geophysical processes using stochastic and machine learning algorithms: Supplementary material , 2017 .
[26] Jian Hu,et al. EMD-KNN Model for Annual Average Rainfall Forecasting , 2013 .
[27] Brian D. Ripley,et al. Feed-Forward Neural Networks and Multinomial Log-Linear Models , 2015 .
[28] Georgia Papacharalampous. Theoretical and empirical comparison of stochastic and machine learning methods for hydrological processes forecasting , 2016 .
[29] Florian Pappenberger,et al. Do probabilistic forecasts lead to better decisions , 2012 .
[30] Rob J Hyndman,et al. Forecasting with Exponential Smoothing: The State Space Approach , 2008 .
[31] Demetris Koutsoyiannis,et al. Hurst‐Kolmogorov Dynamics and Uncertainty 1 , 2011 .
[32] Jean-Philippe Vert,et al. Consistency of Random Forests , 2014, 1405.2881.
[33] N. J. de Vos,et al. Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling , 2013 .
[34] E. S. Gardner. EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .
[35] Richard P. Lippmann,et al. An introduction to computing with neural nets , 1987 .
[36] S. P. Neuman,et al. On model selection criteria in multimodel analysis , 2007 .
[37] H. Akaike. A new look at the statistical model identification , 1974 .
[38] Zaher Mundher Yaseen,et al. Non-tuned machine learning approach for hydrological time series forecasting , 2016, Neural Computing and Applications.
[39] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[40] Hoshin Vijai Gupta,et al. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .
[41] Demetris Koutsoyiannis,et al. On the prediction of persistent processes using the output of deterministic models , 2017 .
[42] Ani Shabri,et al. Streamflow forecasting using least-squares support vector machines , 2012 .
[43] Avi Ostfeld,et al. Data-driven modelling: some past experiences and new approaches , 2008 .
[44] Shie-Yui Liong,et al. FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES 1 , 2002 .
[45] John F. MacGregor,et al. Some Recent Advances in Forecasting and Control , 1968 .
[46] Fangqiong Luo,et al. A novel nonlinear combination model based on Support Vector Machine for stock market prediction , 2010, 2010 8th World Congress on Intelligent Control and Automation.
[47] Singh Manjushree,et al. Application of Software Packages for Monthly Stream Flow Forecasting of Kangsabati River in India , 2011 .
[48] Chuntian Cheng,et al. Using support vector machines for long-term discharge prediction , 2006 .
[49] Fionn Murtagh,et al. Multilayer perceptrons for classification and regression , 1991, Neurocomputing.
[50] G. G. Moisen,et al. Classification and regression trees , 2008 .
[51] Stephen R. Marsland,et al. Machine Learning - An Algorithmic Perspective , 2009, Chapman and Hall / CRC machine learning and pattern recognition series.
[52] Junfei Chen,et al. Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast , 2012 .
[53] P. Phillips,et al. Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .
[54] Demetris Koutsoyiannis. “Hurst-Kolomogorov Dynamics and Uncertainty” , 2010 .
[55] Rosangela Ballini,et al. Multi-step-ahead monthly streamflow forecasting by a neurofuzzy network model , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).
[56] J. Nash,et al. River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .
[57] Tao Hong,et al. Probabilistic electric load forecasting: A tutorial review , 2016 .
[58] Fred L. Collopy,et al. Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .
[59] Vladimir Vapnik,et al. An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.
[60] Gianluca Bontempi,et al. Machine Learning Strategies for Time Series Forecasting , 2012, eBISS.
[61] K. Nikolopoulos,et al. The theta model: a decomposition approach to forecasting , 2000 .
[62] Erwan Scornet,et al. Rejoinder on: A random forest guided tour , 2016 .
[63] Wei-Chiang Hong,et al. Rainfall forecasting by technological machine learning models , 2008, Appl. Math. Comput..
[64] Ozgur Kisi,et al. Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .
[65] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[66] Everette S. Gardner,et al. Exponential smoothing: The state of the art , 1985 .
[67] Demetris Koutsoyiannis,et al. A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables , 2014, Climate Dynamics.
[68] Robert E. Criss,et al. Do Nash values have value? Discussion and alternate proposals , 2008 .
[69] Bellie Sivakumar,et al. Chaos theory in geophysics: past, present and future , 2004 .
[70] William N. Venables,et al. Modern Applied Statistics with S , 2010 .
[71] William W. S. Wei,et al. Time series analysis - univariate and multivariate methods , 1989 .
[72] A. H. Murphy,et al. What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting , 1993 .
[73] Demetris Koutsoyiannis,et al. Simultaneous estimation of the parameters of the Hurst–Kolmogorov stochastic process , 2011 .
[74] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[75] Gordon Fraser,et al. Parameter tuning or default values? An empirical investigation in search-based software engineering , 2013, Empirical Software Engineering.
[76] Richard E. Neapolitan,et al. Artificial Intelligence: With an Introduction to Machine Learning, Second Edition , 2018 .
[77] Vujica Yevjevich,et al. Stochastic models in hydrology , 1987 .
[78] Demetris Koutsoyiannis,et al. Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods / Prévision du débit du Nil à moyen terme: une comparaison de méthodes stochastiques et déterministes , 2008 .
[79] Yuan-Fong Su,et al. On the criteria of model performance evaluation for real-time flood forecasting , 2017, Stochastic Environmental Research and Risk Assessment.
[80] Roman Krzysztofowicz,et al. The case for probabilistic forecasting in hydrology , 2001 .
[81] Fotios Petropoulos,et al. forecast: Forecasting functions for time series and linear models , 2018 .
[82] David Terman,et al. State space , 2008, Scholarpedia.
[83] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[84] Evangelos Spiliotis,et al. Statistical and Machine Learning forecasting methods: Concerns and ways forward , 2018, PloS one.
[85] Christina Gloeckner,et al. Modern Applied Statistics With S , 2003 .
[86] Vladimir Cherkassky,et al. The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.
[87] Ronny Berndtsson,et al. Impact of complexity on daily and multi-step forecasting of streamflow with chaotic, stochastic, and black-box models , 2017, Stochastic Environmental Research and Risk Assessment.
[88] Robert J. Abrahart,et al. Neural Network Hydroinformatics: Maintaining Scientific Rigour , 2009 .
[89] Galit Shmueli,et al. To Explain or To Predict? , 2010 .
[90] Shie-Yui Liong,et al. Rainfall and runoff forecasting with SSA-SVM approach , 2001 .
[91] Özgür Kisi,et al. Forecasting daily lake levels using artificial intelligence approaches , 2012, Comput. Geosci..
[92] C. Holt. Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .
[93] Ethem Alpaydin,et al. Introduction to machine learning , 2004, Adaptive computation and machine learning.
[94] Ian M. Mitchell,et al. Reproducible research for scientific computing: Tools and strategies for changing the culture , 2012, Computing in Science & Engineering.
[95] Clifford M. Hurvich,et al. A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION , 1993 .
[96] Min Han,et al. Support Vector Echo-State Machine for Chaotic Time-Series Prediction , 2007, IEEE Transactions on Neural Networks.
[97] Andreas S. Andreou,et al. Nonlinear analysis and forecasting of a brackish Karstic spring , 2000 .
[98] Gérard Biau,et al. Analysis of a Random Forests Model , 2010, J. Mach. Learn. Res..
[99] Erwan Scornet,et al. A random forest guided tour , 2015, TEST.
[100] D. K. Srivastava,et al. Application of ANN for Reservoir Inflow Prediction and Operation , 1999 .
[101] S. Sorooshian,et al. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data , 1996 .
[102] Pinar Donmez,et al. Introduction to Machine Learning, 2nd ed., by Ethem Alpaydın. Cambridge, MA: The MIT Press 2010. ISBN: 978-0-262-01243-0. $54/£ 39.95 + 584 pages , 2013, Nat. Lang. Eng..
[103] Yihui Xie,et al. Dynamic Documents with R and knitr , 2015 .
[104] Lutgarde M. C. Buydens,et al. Using support vector machines for time series prediction , 2003 .
[105] Rob J Hyndman,et al. Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing , 2011 .
[106] Özgür Kisi,et al. Precipitation forecasting by using wavelet-support vector machine conjunction model , 2012, Eng. Appl. Artif. Intell..
[107] Hadley Wickham,et al. Tools to Make Developing R Packages Easier , 2016 .
[108] Andrew Harvey,et al. A unified view of statistical forecasting procedures , 1984 .
[109] Hoshin Vijai Gupta,et al. Do Nash values have value? , 2007 .
[110] David H. Wolpert,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996, Neural Computation.
[111] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[112] Rob J Hyndman,et al. Unmasking the Theta Method , 2003 .
[113] Michael Y. Hu,et al. Forecasting with artificial neural networks: The state of the art , 1997 .
[114] Rob J Hyndman,et al. Automatic Time Series Forecasting: The forecast Package for R , 2008 .
[115] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[116] Holger R. Maier,et al. Improved validation framework and R-package for artificial neural network models , 2017, Environ. Model. Softw..
[117] Lars Schmidt-Thieme,et al. Beyond Manual Tuning of Hyperparameters , 2015, KI - Künstliche Intelligenz.
[118] R. Lippmann,et al. An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.
[119] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[120] A. Zeileis. Econometric Computing with HC and HAC Covariance Matrix Estimators , 2004 .
[121] Spyros Makridakis,et al. Confidence intervals: An empirical investigation of the series in the M-competition , 1987 .
[122] I SapankevychNicholas,et al. Time series prediction using support vector machines , 2009 .
[123] Wayan Firdaus Mahmudy,et al. Drought forecasting using ANFIS on tuban regency, Indonesia , 2017, 2017 International Conference on Sustainable Information Engineering and Technology (SIET).
[124] Richard A. Wasniowski. Using support vector machines in data mining , 2004 .
[125] Hadley Wickham,et al. The Split-Apply-Combine Strategy for Data Analysis , 2011 .
[126] Spyros Makridakis,et al. The M3-Competition: results, conclusions and implications , 2000 .
[127] MohammadSajjad Khan,et al. Application of Support Vector Machine in Lake Water Level Prediction , 2006 .
[128] Murad S. Taqqu,et al. A seasonal fractional ARIMA Model applied to the Nile River monthly flows at Aswan , 2000 .
[129] Demetris Koutsoyiannis,et al. Error Evolution in Multi-Step Ahead Streamflow Forecasting for the Operation of Hydropower Reservoirs , 2017 .
[130] Demetris Koutsoyiannis,et al. Predictability of monthly temperature and precipitation using automatic time series forecasting methods , 2018, Acta Geophysica.
[131] Kohske Takahashi,et al. Create Elegant Data Visualisations Using the Grammar of Graphics [R package ggplot2 version 3.3.2] , 2020 .
[132] V. Singh,et al. Drought Forecasting Using a Hybrid Stochastic and Neural Network Model , 2007 .
[133] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[134] 이의훈. European Geosciences Union General Assembly 2017 참가 후기 , 2017 .
[135] P. Kitanidis,et al. Real‐time forecasting with a conceptual hydrologic model: 2. Applications and results , 1980 .
[136] Galit Shmueli,et al. To Explain or To Predict? , 2010, 1101.0891.
[137] Gunnar Rätsch,et al. Using support vector machines for time series prediction , 1999 .
[138] Peter K. Kitanidis,et al. Real‐time forecasting with a conceptual hydrologic model: 1. Analysis of uncertainty , 1980 .
[139] Gang Luo,et al. A review of automatic selection methods for machine learning algorithms and hyper-parameter values , 2016, Network Modeling Analysis in Health Informatics and Bioinformatics.
[140] Robert Fildes,et al. The evaluation of extrapolative forecasting methods , 1992 .
[141] Paulo Cortez,et al. Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool , 2010, ICDM.
[142] Rangasami L. Kashyap,et al. Optimal Choice of AR and MA Parts in Autoregressive Moving Average Models , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[143] Parthasarathy Ramachandran,et al. A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin , 2014, Water Resources Management.
[144] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[145] Steven P. Millard,et al. EnvStats: An R Package for Environmental Statistics , 2013 .
[146] P. Krause,et al. COMPARISON OF DIFFERENT EFFICIENCY CRITERIA FOR HYDROLOGICAL MODEL ASSESSMENT , 2005 .
[147] Yihui Xie,et al. Dynamic Documents with R and knitr, Second Edition , 2015 .
[148] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[149] Ozgur Kisi,et al. A wavelet-support vector machine conjunction model for monthly streamflow forecasting , 2011 .
[150] Yihui Xie,et al. knitr: A Comprehensive Tool for Reproducible Research in R , 2018, Implementing Reproducible Research.
[151] J. Armstrong,et al. Evaluating Forecasting Methods , 2001 .
[152] D. Legates,et al. Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .
[153] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[154] Terry L. Lanc,et al. The importance of input variables to a neural network fault-diagnostic system for nuclear power plants , 1992 .
[155] Georgia Papacharalampous,et al. Variable Selection in Time Series Forecasting Using Random Forests , 2017, Algorithms.
[156] B. Efron,et al. Simultaneous Estimation of Parameters , 1972 .
[157] Rob J Hyndman,et al. A state space framework for automatic forecasting using exponential smoothing methods , 2002 .
[158] Communicating uncertainty. , 2002, Minnesota medicine.
[159] Edward H. Wiser,et al. Stochastic Models in Hydrology , 1967 .
[160] François Anctil,et al. A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment , 2009 .
[161] Kurt Hornik,et al. kernlab - An S4 Package for Kernel Methods in R , 2004 .
[162] George Athanasopoulos,et al. Forecasting: principles and practice , 2013 .
[163] Chuntian Cheng,et al. A comparison of performance of several artificial intelligence , 2009 .
[164] S. Liong,et al. EC-SVM approach for real-time hydrologic forecasting , 2004 .
[165] Baki Billah,et al. Empirical information criteria for time series forecasting model selection , 2005 .
[166] M. Valipour,et al. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir , 2013 .
[167] Chuntian Cheng,et al. A new indirect multi-step-ahead prediction model for a long-term hydrologic prediction , 2008 .
[168] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .
[169] Demetris Koutsoyiannis,et al. Negligent killing of scientific concepts: the stationarity case , 2015 .
[170] Rob J Hyndman,et al. Prediction intervals for exponential smoothing using two new classes of state space models 30 January 2003 , 2003 .
[171] Duncan Snidal,et al. Rational Deterrence Theory and Comparative Case Studies , 1989, World Politics.
[172] Demetris Koutsoyiannis,et al. Generic and parsimonious stochastic modelling for hydrology and beyond , 2016 .
[173] Rob J Hyndman,et al. Another look at measures of forecast accuracy , 2006 .
[174] Demetris Koutsoyiannis,et al. One-step ahead forecasting of geophysical processes within a purely statistical framework , 2018, Geoscience Letters.
[175] William Stafford Noble,et al. Support vector machine , 2013 .
[176] Guoqiang Peter Zhang,et al. An investigation of neural networks for linear time-series forecasting , 2001, Comput. Oper. Res..
[177] Rob J Hyndman,et al. 25 years of time series forecasting , 2006 .
[178] R. Hyndman. Automatic time series forecasting , 2006 .
[179] Kurt Hornik,et al. The Design and Analysis of Benchmark Experiments , 2005 .
[180] S. Weijs,et al. Why hydrological predictions should be evaluated using information theory , 2010 .
[181] A. Raftery,et al. Space-time modeling with long-memory dependence: assessing Ireland's wind-power resource. Technical report , 1987 .
[182] Rob J. Hyndman,et al. Forecasting with Exponential Smoothing , 2008 .
[183] Brian D. Ripley,et al. Modern Applied Statistics with S Fourth edition , 2002 .
[184] Ozgur Kisi,et al. River Flow Modeling Using Artificial Neural Networks , 2004 .
[185] Ming Ye,et al. Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff , 2003 .