Computer Science ›› 2023, Vol. 50 ›› Issue (11): 41-48.doi: 10.11896/jsjkx.230500231
• Database & Big Data & Data Science • Previous Articles Next Articles
DONG Hongbin, HAN Shuang, FU Qiang
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[1]LIANG Y,KE S,JUNZ,et al.GeoMAN:Multi-level Attention Networks for Geo-sensory Time Series Prediction[C]//Twenty-Seventh International Joint Conference on Artificial Intelligence.2018:3428-3434. [2]YIN C,DAI Q.A deep multivariate time series multi-step forecasting network[J].Applied Intelligence,2022,52(8):8956-8974. [3]CHEN D,CHEN L,ZHANG Y,et al.A Multiscale InteractiveRecurrent Network for Time- Series Forecasting[J].IEEE Transactions on Cybernetics,2022,52(9):8793-8803. [4]CANDANEDO L,FELDHEIM V.Data driven prediction mo-dels of energy use of appliances in a low-energy house[J].Energy and Buildings,2017,140(4):81-97. [5]MA Z,LIU S,GUO G,et al.Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems[J].IEEE Geoscience and Remote Sensing Letters,2022,19:1-5. [6]DU S D,LI T R,YANG Y,et al.A Sequence-to-Sequence Spatial-Temporal Attention Learning Model for Urban Traffic Flow Prediction[J].Journal of Computer Research and Development,2020,57(8):1715-1728. [7] LAI G,CHANG W,YANG Y,et al.Modeling long- and short-term temporal patterns with deep neural networks[C]//Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:Association for Computing Machinery,2018:95-104. [8]SHIH S,SUN F,LEE H.Temporal pattern attention for multivariate time series forecasting[J].Machine Learning,2019,108:1421-1441. [9]CHANG Y,SUN F,WU Y,et al.A Memory-Network Based Solution for Multivariate Time-Series Forecasting[J].arXiv:1809.02105,2018. [10]HUANG S,WANG D,WU X,et al.DSANet:Dual Self-Attention Network for Multivariate Time Series Forecasting[C]//Proceedings ofthe 28th ACM International Conference on Information and Knowledge Management.New York:Association for Computing Machinery,2019:2129-2132. [11]WU Z,PAN S,LONG G,et al.Connecting the Dots:Multiva-riate Time Series Forecasting with Graph Neural Networks[C]//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:Association for Computing Machinery,2020:753-763. [12]JAIN A,KUMAR A M.Hybrid neural network models forhydrologic time series forecasting[J].Applied Soft Computing,2007,7(2):585-592. [13]HUANG G,WANG L.Hybrid Neural Network Models forHydrologic Time Series Forecasting Based on Genetic Algorithm[C]//Proceedings of the 4th International Joint Confe-rence on Computational Sciences and Optimization.New York:IEEE Press,2011:1347-1350. [14]FENG X,CHEN J,ZHANG Z,et al.State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network[J].Energy,2021,236:1-10. [15]ZHANG Y,PENG N,DAI M,et al.Memory-Gated Recurrent Networks[C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence.Menlo Park:AAAI Press,2021:10956-10963. [16]MA Q,LIN Z,CHEN E,et al.Temporal pyramid recurrent neural network[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence.Menlo Park:AAAI Press,2020:5061-5068. [17]WANG X,ZHANG M,REN F.Sparse Gaussian ConditionalRandom Fields on top of Recurrent Neural Networks[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence.Menlo Park:AAAI Press,2018:4219-4226. [18]MALDONADO S,GONZALEZ A,CRONE S.Automatic time series analysis for electric load forecasting via support vector regression[J].Applied Soft Computing,2019,83(3):1-10. [19]LIU J X,CHEN S.Non-Stationary Multi-variate Time SeriesPrediction with MIX Gated Unit[J].Journal of Computer Research and Development,2019,56(8):1642-1651. [20]LI Z X,LIU H Y.Combining Global and Sequential Patterns for Multivariate Time Series Forecasting[J].Chinese Journal of Computes,2023,46(1):70-84. [21]WANG J,SUN T,LIU B Y,et al.CLVSA:A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets[C]//Proceedings of International Joint Conference on Artificial Intelligence.San Francisco:Morgan Kaufmann.2021:3705-3711. [22]OZDEMIR A,BULUS K,ZOR K.Medium-to long-term nickel price forecasting using LSTM and GRU networks[J].Resources Policy,2022,78:1-10. [23]LIM B,ZOHREN S.Time Series Forecasting With Deep Lear-ning:A Survey[J].Philosophical Transactions of the Royal Society A,2021,379:1-12. [24]GUO T,LIN T,ANTULOV-FANTULIN N.Exploring Inter-pretable LSTM Neural Networks over Multi-Variable Data[C]//Proceedings of the 36th International Conference on Machine Learning.New York:ACM,2019:4424-4440. [25]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].Advances in Neural Information Processing Systems,2017,30:5999-6009. [26]OORD A,DIELMAN S,ZEN H,et al.WaveNet:A Generative Model for Raw Audio[J].arXiv:1609.03499v2,2016. [27]GAN Z,LI C,ZHOU J,et al.Temporal convolutional networks interval prediction model for wind speed forecasting[J].Electric Power Systems Research,2021,191:1-10. [28]LI M,ZHU Z.Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting[C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence.Menlo Park:AAAI Press,2021:4189-4196. [29]ZHANG G P.Time series forecasting using a hybrid ARIMA and neural network model[J].Neurocomputing,2003,50(6):159-175. [30]FRIFOLA R.Bayesian Time Series Learning with GaussianProcesses[D].Cambridgeshire:University of Cambridge,2015. |
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