1 |
ISMAIL FAWAZ H, FORESTIER G, WEBER J, et al. Deep learning for time series classification: a review[J]. Data Mining and Knowledge Discovery, 2019, 33(4): 917-963.
|
2 |
NWEKE H F, WAH T Y, AL-GARADI M, et al. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: state of the art and research challenges [J]. Expert Systems with Applications, 2018, 105: 233-261.
|
3 |
NWE T L, DAT T H, MA B. Convolutional neural network with multi-task learning scheme for acoustic scene classification[C]// Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. Piscataway: IEEE, 2017: 1347-1350.
|
4 |
WANG J, LIU P, SHE M F H, et al. Bag-of-words representation for biomedical time series classification[J]. Biomedical Signal Processing and Control, 2013, 8(6): 634-644.
|
5 |
LINES J, BAGNALL A. Time series classification with ensembles of elastic distance measures[J]. Data Mining and Knowledge Discovery, 2015, 29(3): 565-592.
|
6 |
JI C, ZOU X, HU Y, et al. XG-SF: an XGBoost classifier based on shapelet features for time series classification[J]. Procedia Computer Science, 2019, 147: 24-28.
|
7 |
张雅雯, 王志海, 刘海洋, 等. 基于多尺度残差 FCN 的时间序列分类算法[J]. 软件学报, 2022, 33(2): 555-570.
|
|
ZHANG Y W, WANG Z H, LIU H Y, et al. Time series classification algorithm based on multi-scale residual full convolutional neural network [J]. Journal of Software, 2022, 33(2): 555-570.
|
8 |
王俊陆, 李素, 纪婉婷, 等. 基于 Gram 矩阵的 T-CNN 时间序列分类方法[J]. 浙江大学学报(工学版), 2023, 57(2): 267-276.
|
|
WANG J L, LI S, JI W T, et al. T-CNN time series classification method based on Gram matrix[J]. Journal of Zhejiang University (Engineering Science), 2023, 57(2): 267-276.
|
9 |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 6000-6010.
|
10 |
CHUA L O, ROSKA T. The CNN paradigm[J]. IEEE Transactions on Circuits and Systems Ⅰ: Fundamental Theory and Applications, 1993, 40(3): 147-156.
|
11 |
FABBRI M, MORO G. Dow Jones trading with deep learning: the unreasonable effectiveness of recurrent neural networks[C]// Proceedings of the 7th International Conference on Data Science, Technology and Applications. Setubal, Portugal: SCITEPRESS, 2018: 142-153.
|
12 |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks [C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7132-7141.
|
13 |
LIN M, CHEN Q, YAN S. Network in network [EB/OL]. [2023-06-05]. .
|
14 |
KINGMA D P, BA J. Adam: a method for stochastic optimization [EB/OL]. [2023-06-05]. .
|
15 |
LIAW A, WIENER M. Classification and regression by randomForest [J]. R News, 2002, 2/3: 18-22.
|
16 |
CHEN T, HE T, BENESTY M, et al. XGboost: extreme gradient boosting [EB/OL]. [2023-05-02]. .
|
17 |
RAMCHOUN H, IDRISSI M A J, GHANOU Y, et al. Multilayer perceptron: architecture optimization and training[J]. International Journal of Interactive Multimedia and Artificial Intelligence, 2016, 4(1): 26-30.
|
18 |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
|
19 |
YAO H, TANG X, WEI H, et al. Modeling spatial-temporal dynamics for traffic prediction [EB/OL]. [2023-06-05]. .
|
20 |
LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation [C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2015: 3431-3440.
|