[1] |
NAEEM M , JAMAL T , DIAZ-MARTINEZ J ,et al. Trends and future perspective challenges in big data[C]// Advances in Intelligent Data Analysis and Applications. Berlin:Springer, 2022: 309-325.
|
[2] |
HE H T , GAO S C , JIN T ,et al. A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction[J]. Applied Soft Computing, 2021,108:107488.
|
[3] |
ZHANG M , LIN H , LONG X R ,et al. Analyzing the spatiotemporal pattern and driving factors of wetland vegetation changes using 2000—2019 time-series Landsat data[J]. Science of the Total Environment, 2021,780:146615.
|
[4] |
AL-SHABANDAR R , JADDOA A , LIATSIS P ,et al. A deep gated recurrent neural network for petroleum production forecasting[J]. Machine Learning With Applications, 2021,3:100013.
|
[5] |
BALLI S . Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods[J]. Chaos,Solitons & Fractals, 2021,142:110512.
|
[6] |
THAPA C , CAMTEPE S . Precision health data:requirements,challenges and existing techniques for data security and privacy[J]. Computers in Biology and Medicine, 2021,129:104130.
|
[7] |
HUANG T G , CHAKRABORTY P , SHARMA A . Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images[J]. International Journal of Transportation Science and Technology, 2021:doi.org/10.1016/j.ijtst.2021.10.007.
|
[8] |
IWANA B K , UCHIDA S . An empirical survey of data augmentation for time series classification with neural networks[J]. PLoSOne, 2021,16(7): e0254841.
|
[9] |
KINGMA D P , WELLING M . Auto-encoding variational Bayes[J]. arXiv Preprint,arXiv:1312.6114, 2013.
|
[10] |
GOODFELLOW I , POUGET-ABADIE J , MIRZA M ,et al. Generative adversarial networks[J]. Communications of the ACM, 2020,63(11): 139-144.
|
[11] |
RADFORD A , METZ L , CHINTALA S . Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv Preprint,arXiv:1511.06434, 2015.
|
[12] |
ARJOVSKY M , CHINTALA S , BOTTOU L . Wasserstein generative adversarial networks[C]// Proceedings of the 34th International Conference on Machine Learning. [S.l.]:JMLR, 2017: 214-223.
|
[13] |
ISOLA P , ZHU J Y , ZHOU T H ,et al. Image-to-image translation with conditional adversarial networks[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017: 5967-5976.
|
[14] |
ZHU J Y , PARK T , ISOLA P ,et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]// Proceedings of 2017 IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017: 2242-2251.
|
[15] |
KARRAS T , LAINE S , AILA T M . A style-based generator architecture for generative adversarial networks[C]// Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2019: 4396-4405.
|
[16] |
MEDSKER L R , JAIN L C . Recurrent neural networks[J]. Design and Applications, 2001,5: 64-67.
|
[17] |
GRAVES A . Long short-term memory[J]. Supervised Sequence Labelling with Recurrent Neural Networks, 2012,385: 37-45.
|
[18] |
MOGREN O . C-RNN-GAN:continuous recurrent neural networks with adversarial training[J]. arXiv Preprint,arXiv:1611.09904, 2016.
|
[19] |
ESTEBAN C , HYLAND S L , R?TSCH G , . Real-valued (medical) time series generation with recurrent conditional GANs[J]. arXiv Preprint,arXiv:1706.02633, 2017.
|
[20] |
YOON J , JARRETT D , VAN DER SCHAAR M . Time-series generative adversarial networks[C]// Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).[S.l.:s.n.], 2019: 1-11.
|
[21] |
NI H , SZPRUCH L , WIESE M ,et al. Conditional Sig-Wasserstein GANs for time series generation[J]. SSRN Electronic Journal, 2020:doi.org/10.2139/ssrn.3623086.
|
[22] |
OPPENHEIM A V , WILLSKY A S , NAWAB S H ,et al. Signals &systems[M]. New York: Pearson Educación, 1997.
|
[23] |
BAROT T , BURGSTEINER H , KOLLERITSCH W . Comparison of discrete autocorrelation functions with regards to statistical significance[C]// Applied Informatics and Cybernetics in Intelligent Systems. Berlin:Springer, 2020: 257-266.
|
[24] |
KINGMA D P , BA J . Adam:a method for stochastic optimization[J]. arXiv Preprint,arXiv:1412.6980, 2014.
|