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Aasim, S.N. Singh, and A. Mohapatra, Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting, Renewable Energy, vol. 136, no. 6, pp. 758–768, 2019.AasimS.N. SinghMohapatraA.Repeated wavelet transform based ARIMA model for very short-term wind speed forecastingRenewable Energy1366758768201910.1016/j.renene.2019.01.031Search in Google Scholar
Qi liu, Guanlan Zhang, Shahzad Ali, Xiaopeng Wang, Guodong Wang, Zhenkuan Pan, and Jiahua Zhang, SPI-based drought simulation and prediction using ARMA-GARCH model, Applied Mathematics and Computation, vol. 355, no. 8, pp. 96–107, 2019.LiuQiZhangGuanlanAliShahzadWangXiaopengWangGuodongPanZhenkuanZhangJiahuaSPI-based drought simulation and prediction using ARMA-GARCH modelApplied Mathematics and Computation355896107201910.1016/j.amc.2019.02.058Search in Google Scholar
Clément Cerovecki, Christian Francq, Siegfried Hörmann, and Jean-Michel Zakoïan, Functional GARCH models: The quasi-likelihood approach and its applications, Journal of Econometrics, vol. 209, no. 2, pp. 353–375, 2019.CeroveckiClémentFrancqChristianHörmannSiegfriedZakoïanJean-MichelFunctional GARCH models: The quasi-likelihood approach and its applicationsJournal of Econometrics2092353375201910.1016/j.jeconom.2019.01.006Search in Google Scholar
S. Sivakumar and S. Sivakumar, Marginally Stable Triangular Recurrent Neural Network Architecture for Time Series Prediction, IEEE Transactions on Cybernetics, vol. 48, no. 10, pp. 2836–2850, 2018.SivakumarS.SivakumarS.Marginally Stable Triangular Recurrent Neural Network Architecture for Time Series PredictionIEEE Transactions on Cybernetics481028362850201810.1109/TCYB.2017.2751005Search in Google Scholar
P. Liu, Z. Zeng and J. Wang, Multistability of Recurrent Neural Networks With Nonmonotonic Activation Functions and Unbounded Time-Varying Delays, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, pp. 3000–3010, 2018.LiuP.ZengZ.WangJ.Multistability of Recurrent Neural Networks With Nonmonotonic Activation Functions and Unbounded Time-Varying DelaysIEEE Transactions on Neural Networks and Learning Systems29730003010201810.1109/TNNLS.2017.2710299Search in Google Scholar
W. Lee, K. Kim, J. Park, J. Kim and Y. Kim, Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks, IEEE Access, vol. 6, no. 11, pp. 73068–73080, 2018.LeeW.KimK.ParkJ.KimJ.KimY.Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural NetworksIEEE Access6117306873080201810.1109/ACCESS.2018.2883330Search in Google Scholar
Q. Zhang, H. Wang, J. Dong, G. Zhong and X. Sun, Prediction of Sea Surface Temperature Using Long Short-Term Memory, IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 10, pp. 1745–1749, 2017.ZhangQ.WangH.DongJ.ZhongG.SunX.Prediction of Sea Surface Temperature Using Long Short-Term MemoryIEEE Geoscience and Remote Sensing Letters141017451749201710.1109/LGRS.2017.2733548Search in Google Scholar
M. Ravanelli, P. Brakel, M. Omologo and Y. Bengio, Light Gated Recurrent Units for Speech Recognition, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 2, pp. 92–102, 2018.RavanelliM.BrakelP.OmologoM.BengioY.Light Gated Recurrent Units for Speech RecognitionIEEE Transactions on Emerging Topics in Computational Intelligence2292102201810.1109/TETCI.2017.2762739Search in Google Scholar
R. Zhao, D. Wang, R. Yan, K. Mao, F. Shen and J. Wang, Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks, IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1539–1548, 2018.ZhaoR.WangD.YanR.MaoK.ShenF.WangJ.Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit NetworksIEEE Transactions on Industrial Electronics65215391548201810.1109/TIE.2017.2733438Search in Google Scholar
A.Y. Alanis, Electricity Prices Forecasting using Artificial Neural Networks, IEEE Latin America Transactions, vol. 16, no.1, pp. 105–111, 2018.AlanisA.Y.Electricity Prices Forecasting using Artificial Neural NetworksIEEE Latin America Transactions161105111201810.1109/TLA.2018.8291461Search in Google Scholar
L. Chen, Z. Qiao, M. Wang, C. Wang, R. Du and H. E. Stanley, Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market, IEEE Access, vol. 6, no. 8, pp. 48625–48633, 2018.ChenL.QiaoZ.WangM.WangC.DuR.StanleyH. E.Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock MarketIEEE Access684862548633201810.1109/ACCESS.2018.2859809Search in Google Scholar
J. Tang, C. Deng and G. Huang, Extreme Learning Machine for Multilayer Perceptron, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, pp. 809–821, 2016.TangJ.DengC.HuangG.Extreme Learning Machine for Multilayer PerceptronIEEE Transactions on Neural Networks and Learning Systems274809821201610.1109/TNNLS.2015.2424995Search in Google Scholar
L. Grippo, A. Manno and M. Sciandrone, Decomposition Techniques for Multilayer Perceptron Training, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 11, pp. 2146–2159, 2016.GrippoL.MannoA.SciandroneM.Decomposition Techniques for Multilayer Perceptron TrainingIEEE Transactions on Neural Networks and Learning Systems271121462159201610.1109/TNNLS.2015.2475621Search in Google Scholar
Lili Mo, Ling Xie, Xiaoyi Jiang, Geer Teng, Lixiang Xu and Jin Xiao, GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries, Applied Soft Computing, vol. 62, no. 1, pp. 478–490, 2018.MoLiliXieLingJiangXiaoyiTengGeerXuLixiangXiaoJinGMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseriesApplied Soft Computing621478490201810.1016/j.asoc.2017.10.033Search in Google Scholar