[1] |
Whittle P.Hypothesis testing in time series analysis[J].Journal of the Royal Statistical Society Series A (General), 1951, 114(4):579.
|
[2] |
Box G E P,Jenkins G M, Reinsel G C, et al.Time series analysis:Forecasting and control[M].5th ed. New Jersey:John Wiley and Sons Inc.,2015.
|
[3] |
Cao L J,Tay F E H.Support vector machine with adaptive parameters in financial time series forecasting[J].IEEE Transactions on Neural Networks,2003,14(6):1506-1518.
|
[4] |
Bollerslev T. Generalized autoregressive conditional hetero- skedasticity[J].Journal of Econometrics,1986,31(3):307- 327.
|
[5] |
Ariyo A A,Adewumi A O,Ayo C K.Stock price prediction using the ARIMA model[C]∥Proc of 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation,2014:106-112.
|
[6] |
Karim F,Majumdar S,Darabi H,et al.Multivariate LSTM-FCNs for time series classification[J].Neural Networks,2019,116:237-245.
|
[7] |
Yu B, Yin H,Zhu Z.Spatio-temporal graph convolutional networks:A deep learning framework for traffic forecasting[J].arXiv:1709.04875,2017.
|
[8] |
Meesad P,Rasel R I.Predicting stock market price using support vector regression[C]∥Proc of 2013 International Conference on Informatics,Electronics and Vision,2013:1-6.
|
[9] |
Nair B, Mohandas V P,Sakthivel N R.A decision tree-rough set hybrid system for stock market trend prediction[J].International Journal of Computer Applications,2010,6(9):1-6.
|
[10] |
White H.Economic prediction using neural networks:The case of IBM daily stock returns[C]∥Proc of IEEE 1988 International Conference on Neural Networks,1988:451-458.
|
[11] |
Selvin S,Vinayakumar R,Gopalakrishnan E A,et al.Stock price prediction using LSTM,RNN and CNN-sliding window model[C]∥Proc of 2017 International Conference on Advances in Computing,Communications and Informatics,2017:1643-1647.
|
[12] |
Kussul N, Shelestov A,Lavreniuk M,et al.Deep learning approach for large scale land cover mapping based on remote sensing data fusion[C]∥Proc of 2016 IEEE International Geoscience and Remote Sensing Symposium,2016:198-201.
|
[13] |
李章晓,宋微,田野.基于深度学习和进化计算的外汇预测与投资组合优化[J].郑州大学学报(工学版),2019,40(1):92-96.
|
|
Li Zhang-xiao,Song Wei,Tian Ye. Exchange rate forecasting and portfolio optimization based on deep learning and evolutionary computation[J].Journal of Zhengzhou University (Engineering Science),2019,40(1):92-96.
|
[14] |
杨妥,李万龙,郑山红.融合情感分析与SVM_LSTM模型的股票指数预测[J].软件导刊,2020,19(8):14-18.
|
|
Yang Tuo,Li Wan-long,Zheng Shan-hong.Stock index prediction based on SVM _LSTM model with emotion analysis[J] Software Guide,2020,19(8):14-18.
|
[15] |
Wu Y, Tan H. Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework[J].arXiv:1612.01022,2016.
|
[16] |
Gensler A,Henze J,Sick B,et al.Deep learning for solar power forecasting—An approach using AutoEncoder and LSTM neural networks[C]∥Proc of 2016 IEEE International Conference on Systems,Man,and Cybernetics,2016:2858-2865.
|
[17] |
Grover A,Kapoor A,Horvitz E.A deep hybrid model for weather forecasting[C]∥Proc of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2015:379-386.
|
[18] |
Holland J H.Adaptation in natural and artificial systems:An introductory analysis with applications to biology,control,and artificial intelligence[M].Cambridge:MIT Press,1992.
|
[19] |
Zhou H,Zhang S,Peng J,et al.Informer:Beyond efficient transformer for long sequence time-series forecasting [C]∥Proc of the 35th AAAI Conference on Artificial Intelligence,2021:11106-11115.
|
[20] |
Taylor S J,Letham B.Forecasting at scale[J].The American Statistician,2018,72(1):37-45.
|
[21] |
Bahdanau D,Cho K H,Bengio Y.Neural machine translation by jointly learning to align and translate[C]∥Proc of the 3rd International Conference on Learning Representations,2015:1.
|
[22] |
Salinas D,Flunkert V,Gasthaus J,et al.DeepAR:Probabilistic forecasting with autoregressive recurrent networks[J].International Journal of Forecasting,2020,36(3):1181-1191.
|
[23] |
Kitaev N, Kaiser ,Levskaya A.Reformer:The efficient transformer[J].arXiv:2001.04451,2020.
|
[24] |
Li S Y,Jin X Y,Xuan Y,et al.Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting [C]∥Proc of the 33rd International Confe- rence on Neural Information Processing Systems,2019: 5243-5253.
|