[1] POTDAR K, KINNERKAR R.A non-linear autoregressive neural network model for forecasting Indian index of industrial production[C]//Proceedings of IEEE Region 10 Symposium.Washington D.C., USA:IEEE Press, 2017:1-5. [2] HANSSENS D M.Market response, competitive behavior, and time-series analysis[M].[S.l.]:World Scientific, 2018. [3] BAYRAMOGLU T, ARI Y O.The relationship between tourism and economic growth in Greece economy:a time series analysis[J].Computational Methods in Social Sciences, 2015, 3(1):89-94. [4] 陈艳, 王子健, 赵泽, 等.传感器网络环境监测时间序列数据的高斯过程建模与多步预测[J].通信学报, 2015, 36(10):252-262. CHEN Y, WANG Z J, ZHAO Z, et al.Gaussian process modeling and multi-step prediction for time series data in wireless sensor network environmental monitoring[J].Journal on Communications, 2015, 36(10):252-262.(in Chinese) [5] WAN H Y, GUO S N, YIN K, et al.CTS-LSTM:LSTM-based neural networks for correlated time series prediction[J].Knowledge-Based Systems, 2020, 191:105239. [6] LI P X, TAN Z X, YAN L L, et al.Time series prediction of mining subsidence based on a SVM[J].Mining Science and Technology(China), 2011, 21(4):557-562. [7] 潘臻.基于隐马尔可夫模型的代码仓库审查时间预测方法[D].南京:南京邮电大学, 2020. PAN Z.Code review time prediction method based on hidden Markov model[D].Nanjing:Nanjing University of Posts and Telecommunications, 2020.(in Chinese) [8] TOKGÖZ A, ÜNAL G.A RNN based time series approach for forecasting Turkish electricity load[C]//Proceedings of the 26th Signal Processing and Communications Applications Conference(SIU).Washington D.C., USA:IEEE Press, 2018:1-4. [9] SIAMI-NAMINI S, TAVAKOLI N, SIAMI NAMIN A.A comparison of ARIMA and LSTM in forecasting time series[C]//Proceedings of the 17th IEEE International Conference on Machine Learning and Applications.Washington D.C., USA:IEEE Press, 2018:1394-1401. [10] XIE H L, ZHANG L, LIM C P.Evolving CNN-LSTM models for time series prediction using enhanced grey wolf optimizer[J].IEEE Access, 2020, 8:161519-161541. [11] SAJJAD M, KHAN Z A, ULLAH A, et al.A novel CNN-GRU-based hybrid approach for short-term residential load forecasting[J].IEEE Access, 2020, 8:143759-143768. [12] JIN X B, YU X H, WANG X Y, et al.Prediction for Time Series with CNN and LSTM[C]//Proceedings of the 11th International Conference on Modelling, Identification and Control.Berlin, Germany:Springer, 2020:631-641. [13] HUANG C J, KUO P H.A deep CNN-LSTM model for particulate matter(PM2.5) forecasting in smart cities[J].Sensors(Basel, Switzerland), 2018, 18(7):2220-2232. [14] CHEN Y R, WANG Y, DONG Z K, et al.2-D regional short-term wind speed forecast based on CNN-LSTM deep learning model[J].Energy Conversion and Management, 2021, 244:114451. [15] WU Q Y, GUAN F, LÜ C, et al.Ultra-short-term multi-step wind power forecasting based on CNN-LSTM[J].IET Renewable Power Generation, 2021, 15(5):1019-1029. [16] LAI G K, CHANG W C, YANG Y M, et al.Modeling long- and short-term temporal patterns with deep neural networks[C]//Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval.New York, USA:ACM Press, 2018:95-104. [17] SHIH S Y, SUN F K, LEE H Y.Temporal pattern attention for multivariate time series forecasting[J].Machine Learning, 2019, 108(8/9):1421-1441. [18] HUANG S T, WANG D L, WU X H, et al.DSANet:dual self-attention network for multivariate time series forecasting[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.New York, USA:ACM Press, 2019:2129-2132. [19] BAI S J, KOLTER J Z, KOLTUN V.An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[EB/OL].[2021-12-04].https://arxiv.org/abs/1803.01271. [20] FAN J, ZHANG K, HUANG Y P, et al.Parallel spatio-temporal attention-based TCN for multivariate time series prediction[J].Neural Computing and Applications, 2022, 34(24):21419-21639. [21] ZHAO B D, LU H Z, CHEN S F, et al.Convolutional neural networks for time series classification[J].Journal of Systems Engineering and Electronics, 2017, 28(1):162-169. [22] WANG L X.Fast training algorithms for deep convolutional fuzzy systems with application to stock index prediction[J].IEEE Transactions on Fuzzy Systems, 2020, 28(7):1301-1314. [23] WILSON K W, RAJ B, SMARAGDIS P.Regularized non-negative matrix factorization with temporal dependencies for speech denoising[C]//Proceedings of InterSpeech 2008.Brisbane, Australia:[s.n.], 2008:411-414. [24] YU B, YIN H, ZHU Z.Spatio-temporal graph convolutional networks:a deep learning framework for traffic forecasting[EB/OL].[2021-12-04].https://arxiv.org/abs/1709.04875. [25] SEN R, YU H F, DHILLON I.Think globally, act locally:a deep neural network approach to high-dimensional time series forecasting[EB/OL].[2021-12-04].https://www.cnblogs.com/dulun/p/12271730.html. [26] BAI Y T, JIN X B, WANG X Y, et al.Compound autoregressive network for prediction of multivariate time series[EB/OL].[2021-12-04].https://doi.org/10.1155/2019/9107167. [27] VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[EB/OL].[2021-12-04].https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html. [28] BACHE K, LICHMAN M.UCI machine learning repository[EB/OL].[2021-12-04].https://archive.ics.uci.edu/ml/index.php. |