A three-stage multi-objective heterogeneous integrated model with decomposition-reconstruction mechanism and adaptive segmentation error correction method for ship motion multi-step prediction

[1]  Xi Chen,et al.  A time-varying ensemble model for ship motion prediction based on feature selection and clustering methods , 2023, Ocean Engineering.

[2]  Zezong Chen,et al.  Deterministic ship roll forecasting model based on multi-objective data fusion and multi-layer error correction , 2022, Appl. Soft Comput..

[3]  Xi Chen,et al.  Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved QRNN , 2022, Adv. Eng. Informatics.

[4]  Zezong Chen,et al.  An Ensemble Multi-Step Forecasting Model for Ship Roll Motion Under Different External Conditions: A Case Study on the South China Sea , 2022, SSRN Electronic Journal.

[5]  Jianghong Zhou,et al.  Remaining useful life prediction of bearings by a new reinforced memory GRU network , 2022, Adv. Eng. Informatics.

[6]  Xi Chen,et al.  Big multi-step ship motion forecasting using a novel hybrid model based on real-time decomposition, boosting algorithm and error correction framework , 2022, Ocean Engineering.

[7]  Rui Yang,et al.  A BiLSTM hybrid model for ship roll multi-step forecasting based on decomposition and hyperparameter optimization , 2021, Ocean Engineering.

[8]  Wei‐Chiang Hong,et al.  A hybrid approach for forecasting ship motion using CNN-GRU-AM and GCWOA , 2021, Appl. Soft Comput..

[9]  John Quilty,et al.  A maximal overlap discrete wavelet packet transform integrated approach for rainfall forecasting - A case study in the Awash River Basin (Ethiopia) , 2021, Environ. Model. Softw..

[10]  Lukumon O. Oyedele,et al.  Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm , 2021, Adv. Eng. Informatics.

[11]  Tzu-Li Chen,et al.  Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery , 2021, Adv. Eng. Informatics.

[12]  Hui Liu,et al.  Hourly PM2.5 concentration multi-step forecasting method based on extreme learning machine, boosting algorithm and error correction model , 2021, Digit. Signal Process..

[13]  Tao Zhang,et al.  Multiscale attention-based LSTM for ship motion prediction , 2021 .

[14]  Huixuan Fu,et al.  Ship Roll Prediction Algorithm Based on Bi-LSTM-TPA Combined Model , 2021, Journal of Marine Science and Engineering.

[15]  Mingxi Liu,et al.  Optimal selection of heterogeneous ensemble strategies of time series forecasting with multi-objective programming , 2021, Expert Syst. Appl..

[16]  Dingjie Xu,et al.  An EMD-SVR model for short-term prediction of ship motion using mirror symmetry and SVR algorithms to eliminate EMD boundary effect , 2020 .

[17]  Viviana Cocco Mariani,et al.  A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting , 2020 .

[18]  Ying Wang,et al.  A novel hybrid air quality early-warning system based on phase-space reconstruction and multi-objective optimization: A case study in China , 2020 .

[19]  Heng Yang,et al.  Scale effects in AR model real-time ship motion prediction , 2020 .

[20]  Chao Chen,et al.  A novel axle temperature forecasting method based on decomposition, reinforcement learning optimization and neural network , 2020, Adv. Eng. Informatics.

[21]  Haiping Wu,et al.  A novel two-stage deep learning wind speed forecasting method with adaptive multiple error corrections and bivariate Dirichlet process mixture model , 2019, Energy Conversion and Management.

[22]  Ramchandra Manthalkar,et al.  Time series decomposition and predictive analytics using MapReduce framework , 2019, Expert Syst. Appl..

[23]  Anastassios N. Perakis,et al.  A real-time ship roll motion prediction using wavelet transform and variable RBF network , 2018, Ocean Engineering.

[24]  Cheng Liu,et al.  Research and application of ensemble forecasting based on a novel multi-objective optimization algorithm for wind-speed forecasting , 2017 .

[25]  Adekunle Ayodotun Osinowo,et al.  Statistical analyses of sea state conditions in South China Sea , 2017, Journal of Ocean University of China.

[26]  Jianzhou Wang,et al.  A self-adaptive hybrid approach for wind speed forecasting , 2015 .

[27]  Kwok-wing Chau,et al.  Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition , 2015, Water Resources Management.

[28]  Minxia Luo,et al.  Outlier-robust extreme learning machine for regression problems , 2015, Neurocomputing.

[29]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[30]  Zao-Jian Zou,et al.  On-line prediction of ship roll motion during maneuvering using sequential learning RBF neuralnetworks , 2013 .

[31]  José Manuel Benítez,et al.  On the use of cross-validation for time series predictor evaluation , 2012, Inf. Sci..

[32]  Gianluca Bontempi,et al.  Conditionally dependent strategies for multiple-step-ahead prediction in local learning , 2011 .

[33]  P. Shan,et al.  Nonlinear Time-Varying Spectral Analysis: HHT and MODWPT , 2010 .

[34]  Chien Ming Wang,et al.  Hydrodynamic forces on a rolling barge with bilge keels , 2010 .

[35]  Junfeng Yang,et al.  Alternating Direction Algorithms for 1-Problems in Compressive Sensing , 2009, SIAM J. Sci. Comput..

[36]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[37]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[38]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[39]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[40]  J. Salas,et al.  Nonlinear dynamics, delay times, and embedding windows , 1999 .

[41]  Min Xie,et al.  The use of ARIMA models for reliability forecasting and analysis , 1998 .

[42]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[43]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[44]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[45]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[46]  Paul Kaplan,et al.  A PRELIMINARY STUDY OF PREDICTION TECHNIQUES FOR AIRCRAFT CARRIER MOTIONS AT SEA , 1965 .

[47]  Dedy Dwi Prastyo,et al.  Roll motion prediction using a hybrid deep learning and ARIMA model , 2018, INNS Conference on Big Data.

[48]  Raja Ariffin Raja Ghazilla,et al.  Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications , 2013, SiPS 2013.

[49]  Héctor Pomares,et al.  Soft-computing techniques and ARMA model for time series prediction , 2008, Neurocomputing.