Systems Engineering and Electronics ›› 2023, Vol. 45 ›› Issue (2): 313-335.doi: 10.12305/j.issn.1001-506X.2023.02.01
• Electronic Technology •
Survey of univariate sequence data classification methods
Ye ZHANG, Yi HOU, Kewei OUYANG, Shilin ZHOU
- College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
-
Received:2021-04-06Online:2023-01-13Published:2023-02-04 -
Contact:Yi HOU
CLC Number:
Cite this article
Ye ZHANG, Yi HOU, Kewei OUYANG, Shilin ZHOU. Survey of univariate sequence data classification methods[J]. Systems Engineering and Electronics, 2023, 45(2): 313-335.
share this article
Fig.1
Framework of univariate sequence data classification methods"
Fig.1
Fig.2
Example diagrams of lockstep and elastic distance metrics"
Fig.2
Fig.3
Diagrams of two constrained windows for DTW metrics"
Fig.3
Fig.4
Example diagrams of Shapelets"
Fig.4
Fig.5
Example diagram of mapping relationship between DFT parameters and symbolic features"
Fig.5
Fig.6
Example diagram of symbolic histogram"
Fig.6
Fig.7
Framework diagram of mWDN"
Fig.7
Fig.8
Raw sequence data and its GASF, GADF and MTF transformed images"
Fig.8
Fig.9
Raw sequence data and its RP transformed images"
Fig.9
Fig.10
Structure diagram of RNN and LSTM"
Fig.10
Fig.11
Graphical illustration of COTE ensemble classifier"
Fig.11
Fig.12
Graphical illustration of HIVE-COTE ensemble classifier"
Fig.12
Fig.13
Example diagrams of sequence data augmentation methods based on shape transformation"
Fig.13
Fig.14
Example diagrams of several sequence data types"
Fig.14
Fig.15
Ciritical difference diagrams of shape information based classification methods"
Fig.15
Fig.16
Critical difference diagram of classification methods based on frequency information"
Fig.16
Fig.17
Critical difference diagram of classification methods based on context information"
Fig.17
Fig.18
Critical difference diagram of classification methods based on information fusion"
Fig.18
Fig.19
Critical difference diagram of different TSC methods"
Fig.19
Fig.20
Critical difference diagrams of two groups of classification methods on image-to-sequence datasets"
Fig.20
Fig.21
Critical difference diagrams of two groups of classification methods on sensor datasets"
Fig.21
Fig.22
Critical difference diagrams of two groups of classification methods on electrical deveice datasets"
Fig.22
Fig.23
Critical difference diagrams of two groups of classification methods on human activity datasets"
Fig.23
| 1 |
吴子龙, 陈红, 雷迎科, 等. 基于堆栈式LSTM网络的通信辐射源个体识别[J]. 系统工程与电子技术, 2020, 42 (12): 2915- 2923.
doi: 10.3969/j.issn.1001-506X.2020.12.30 |
|
WU Z L , CHEN H , LEI Y K , et al. Communication emitter individual identification based on stacked LSTM network[J]. Systems Engineering and Electronics, 2020, 42 (12): 2915- 2923.
doi: 10.3969/j.issn.1001-506X.2020.12.30 |
|
| 2 | 孙艺聪, 田润澜, 王晓峰, 等. 基于改进CLDNN的辐射源信号识别[J]. 系统工程与电子技术, 2021, 43 (1): 42- 47. |
| SUN Y C , TIAN R L , WANG X F , et al. Emitter signal recognition based on improved CLDNN[J]. Systems Engineering and Electronics, 2021, 43 (1): 42- 47. | |
| 3 |
孙世岩, 张钢, 田福庆, 等. 多输入混合深度学习网络的健康因子构建方法[J]. 系统工程与电子技术, 2020, 42 (10): 2390- 2398.
doi: 10.3969/j.issn.1001-506X.2020.10.30 |
|
SUN S Y , ZHANG G , TIAN F Q , et al. Health indicator construction method of multi-input hybrid deep learning network[J]. Systems Engineering and Electronics, 2020, 42 (10): 2390- 2398.
doi: 10.3969/j.issn.1001-506X.2020.10.30 |
|
| 4 |
RUIZ A P , FLYNN M , LARGE J , et al. The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances[J]. Data Mining and Knowledge Discovery, 2021, 35 (2): 401- 449.
doi: 10.1007/s10618-020-00727-3 |
| 5 |
FAWAZ H I , 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.
doi: 10.1007/s10618-019-00619-1 |
| 6 |
BAGNALL A , LINES J , BOSTROM A , et al. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances[J]. Data Mining and Know-ledge Discovery, 2017, 31 (3): 606- 660.
doi: 10.1007/s10618-016-0483-9 |
| 7 |
李文, 邓升, 段妍, 等. 时间序列预测与深度学习: 文献综述与应用实例[J]. 计算机应用与软件, 2020, 37 (10): 64- 70.
doi: 10.3969/j.issn.1000-386x.2020.10.011 |
|
LI W , DENG S , DUAN Y , et al. Time series forecasting and deep learning: literature review and empirical example[J]. Computer Applications and Software, 2020, 37 (10): 64- 70.
doi: 10.3969/j.issn.1000-386x.2020.10.011 |
|
| 8 | MAHMOUD A, MOHAMMED A. A survey on deep learning for time-series forecasting[M]//Machine learning and big data analytics paradigms: analysis, applications and challenges. Cham: Springer, 2021: 365-392. |
| 9 | PANG G S , SHEN C H , CAO L B , et al. Deep learning for anomaly detection: a review[J]. ACM Computing Surveys, 2021, 54 (2): 1- 38. |
| 10 | 丁小欧, 于晟健, 王沐贤, 等. 基于相关性分析的工业时序数据异常检测[J]. 软件学报, 2020, 31 (3): 726- 747. |
| DING X O , YU S J , WANG M X , et al. Anomaly detection on industrial time series based on correlation analysis[J]. Journal of Software, 2020, 31 (3): 726- 747. | |
| 11 | 胡珉, 白雪, 徐伟, 等. 多维时间序列异常检测算法综述[J]. 计算机应用, 2020, 40 (6): 1553- 1564. |
| HU M , BAI X , XU W , et al. Review of anomaly detection algorithms for multidimensional time series[J]. Journal of Computer Applications, 2020, 40 (6): 1553- 1564. | |
| 12 | ESLING P , AGON C . Time-series data mining[J]. ACM Computing Surveys, 2012, 45 (1): 1- 34. |
| 13 |
DAU H A , BAGNALL A , KAMGAR K , et al. The UCR time series archive[J]. IEEE/CAA Journal of Automatica Sinica, 2019, 6 (6): 1293- 1305.
doi: 10.1109/JAS.2019.1911747 |
| 14 |
FALOUTSOS C , RANGANATHAN M , MANOLOPOULOS Y . Fast subsequence matching in time-series databases[J]. ACM Sigmod Record, 1994, 23 (2): 419- 429.
doi: 10.1145/191843.191925 |
| 15 | YI B, FALOUTSOS C. Fast time sequence indexing for arbitrary Lp norms[C]//Proc. of the 26th International Conference on Very Large Data Bases, 2000: 385-394. |
| 16 | DAS G, GUNOPULOS D, MANNILA H. Finding similar time series[C]//Proc. of the European Symposium on Principles of Data Mining and Knowledge Discovery, 1997: 88-100. |
| 17 | VLACHOS M, KOLLIOS G, GUNOPULOS D. Discovering similar multidimensional trajectories[C]//Proc. of the 18th International Conference on Data Engineering, 2002: 673-684. |
| 18 | CHEN L, ÖZSU M T, ORIA V. Robust and fast similarity search for moving object trajectories[C]//Proc. of the ACM SIGMOD International Conference on Management of Data, 2005: 491-502. |
| 19 | CHEN L, NG R. On the marriage of Lp-norms and edit distance[C]//Proc. of the 30th International Conference on Very Large Data Bases, 2004: 792-803. |
| 20 | STEFAN A , ATHITSOS V , DAS G . The move-split-merge metric for time series[J]. IEEE Trans.on Knowledge and Data Engineering, 2012, 25 (6): 1425- 1438. |
| 21 | BERNDT D J, CLIFFORD J. Using dynamic time warping to find patterns in time series[C]//Proc. of the 3rd International Conference on Knowledge Discovery and Data Mining, 1994: 359-370. |
| 22 |
SALVADOR S , CHAN P . Toward accurate dynamic time warping in linear time and space[J]. Intelligent Data Analysis, 2007, 11 (5): 561- 580.
doi: 10.3233/IDA-2007-11508 |
| 23 |
SAKOE H , CHIBA S . Dynamic programming algorithm optimization for spoken word recognition[J]. IEEE Trans.on Acoustics, Speech, and Signal Processing, 1978, 26 (1): 43- 49.
doi: 10.1109/TASSP.1978.1163055 |
| 24 |
JEONG Y S , JEONG M K , OMITAOMU O A . Weighted dynamic time warping for time series classification[J]. Pattern Recognition, 2011, 44 (9): 2231- 2240.
doi: 10.1016/j.patcog.2010.09.022 |
| 25 |
KEOGH E , RATANAMAHATANA C A . Exact indexing of dynamic time warping[J]. Knowledge and Information Systems, 2005, 7 (3): 358- 386.
doi: 10.1007/s10115-004-0154-9 |
| 26 | PETITJEAN F, FORESTIER G, WEBB G I, et al. Dynamic time warping averaging of time series allows faster and more accurate classification[C]//Proc. of the IEEE International Conference on Data Mining, 2014: 470-479. |
| 27 |
PETITJEAN F , KETTERLIN A , GANCARSKI P . A global averaging method for dynamic time warping, with applications to clustering[J]. Pattern Recognition, 2011, 44 (3): 678- 693.
doi: 10.1016/j.patcog.2010.09.013 |
| 28 | JALALIAN A , CHALUP S K . GDTW-P-SVMs: variable-length time series analysis using support vector machines[J]. Neurocomputing, 2013, 99 (1): 270- 282. |
| 29 | JANYALIKIT T, SATHIANWIRIYAKHUN P, SIVARAKS H, et al. An enhanced support vector machine for faster time series classification[C]//Proc. of the Asian Conference on Intelligent Information and Database Systems, 2016: 616-625. |
| 30 |
IWANA B K , FRINKEN V , RIESEN K , et al. Efficient temporal pattern recognition by means of dissimilarity space embedding with discriminative prototypes[J]. Pattern Recognition, 2017, 64, 268- 276.
doi: 10.1016/j.patcog.2016.11.013 |
| 31 |
VIOLA P , JONES M J . Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57 (2): 137- 154.
doi: 10.1023/B:VISI.0000013087.49260.fb |
| 32 |
WANG X , MUEEN A , DING H , et al. Experimental comparison of representation methods and distance measures for time series data[J]. Data Mining and Knowledge Discovery, 2013, 26 (2): 275- 309.
doi: 10.1007/s10618-012-0250-5 |
| 33 |
LINES J , BAGNALL A . Time series classification with ensembles of elastic distance measures[J]. Data Mining and Know-ledge Discovery, 2015, 29 (3): 565- 592.
doi: 10.1007/s10618-014-0361-2 |
| 34 |
LUCAS B , SHIFAZ A , PELLETIER C , et al. Proximity forest: an effective and scalable distance-based classifier for time series[J]. Data Mining and Knowledge Discovery, 2019, 33 (3): 607- 635.
doi: 10.1007/s10618-019-00617-3 |
| 35 | 嵇存. 基于Shapelet的时间序列分类方法研究[D]. 济南: 山东大学, 2017. |
| JI C. Time series classification methods based on shapelet[D]. Jinan: Shandong University, 2017. | |
| 36 | YE L, KEOGH E. Time series shapelets: a new primitive for data mining[C]//Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009: 947-956. |
| 37 |
LIN J , KEOGH E , WEI L , et al. Experiencing SAX: a novel symbolic representation of time series[J]. Data Mining and Knowledge Discovery, 2007, 15 (2): 107- 144.
doi: 10.1007/s10618-007-0064-z |
| 38 | RAKTHANMANON T, KEOGH E. Fast Shapelets: a scalable algorithm for discovering time series Shapelets[C]//Proc. of the SIAM International Conference on Data Mining, 2013: 668-676. |
| 39 | ZHANG Z G, ZHANG H W, WEN Y L, et al. Accelerating time series Shapelets discovery with key points[C]//Proc. of the Asia-Pacific Web Conference, 2016: 330-342. |
| 40 |
HILLS J , LINES J , BARANAUSKAS E , et al. Classification of time series by Shapelet transformation[J]. Data Mining and Knowledge Discovery, 2014, 28 (4): 851- 881.
doi: 10.1007/s10618-013-0322-1 |
| 41 | JI C , ZHAO C , LIU S J , et al. A fast Shapelet selection algorithm for time series classification[J]. Computer Networks, 2019, 148 (15): 231- 240. |
| 42 |
KRUSKAL W H . A nonparametric test for the several sample problem[J]. Annals of Mathematical Statistics, 1952, 23 (4): 525- 540.
doi: 10.1214/aoms/1177729332 |
| 43 | GRABOCKA J, SCHILLING N, WISTUBA M, et al. Learning time-series shapelets[C]//Proc. of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014: 392-401. |
| 44 | MA Q, ZHUANG W, COTTRELL G. Triple-Shapelet networks for time series classification[C]//Proc. of the IEEE International Conference on Data Mining, 2019: 1246-1251. |
| 45 | WANG Y, EMONET R, FROMONT E, et al. Learning interpretable shapelets for time series classification through adversarial regularization[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/1906.00917.pdf. |
| 46 | DENG H , RUNGER G , TUV E , et al. A time series forest for classification and feature extraction[J]. Information Sciences, 2013, 239 (4): 142- 153. |
| 47 | BAYDOGAN M G , RUNGER G , TUV E . A bag-of-features framework to classify time series[J]. IEEE Trans.on Pattern Analysis & Machine Intelligence, 2013, 35 (11): 2796- 2802. |
| 48 | SENIN P, MALINCHIK S. SAX-VSM: interpretable time series classification using SAX and vector space model[C]//Proc. of the IEEE 13th International Conference on Data Mining, 2013: 1175-1180. |
| 49 | CUI Z C, CHEN W L, CHEN Y X. Multi-scale convolutional neural networks for time series classification[EB/OL]. [2021-03-16]. https://arxiv,org/pdf/1603.06995.pdf. |
| 50 | WANG Z, YAN W, OATES T. Time series classification from scratch with deep neural networks: a strong baseline[C]//Proc. of the International Joint Conference on Neural Networks, 2017: 1578-1585. |
| 51 |
FAWAZ H I , LUCAS B , FORESTIER G , et al. Inception time: finding AlexNet for time series classification[J]. Data Mining and Knowledge Discovery, 2020, 34 (6): 1936- 1962.
doi: 10.1007/s10618-020-00710-y |
| 52 | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778. |
| 53 | SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2017. |
| 54 |
SCARDAPANE S , WANG D . Randomness in neural networks: an overview[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2017, 7 (2): e1200.
doi: 10.1002/widm.1200 |
| 55 | SCHÄFER P, HÖGQVIST M. SFA: a symbolic Fourier approximation and index for similarity search in high dimensional datasets[C]//Proc. of the 15th International Conference on Extending Database Technology, 2012: 516-527. |
| 56 |
SCHÄFER P . The BOSS is concerned with time series classification in the presence of noise[J]. Data Mining and Knowledge Discovery, 2015, 29 (6): 1505- 1530.
doi: 10.1007/s10618-014-0377-7 |
| 57 | SCHÄFER P. Bag-of-SFA-symbols in vector space (BOSS VS)[R]. Berlin: Zuse Institute Berlin, 2015: 15-30. |
| 58 | SCHÄFER P, LESER U. Fast and accurate time series classification with weasel[C]//Proc. of the ACM on Conference on Information and Knowledge Management, 2017: 637-646. |
| 59 |
SALTON G , WONG A , YANG C S . A vector space model for automatic indexing[J]. Communications of the ACM, 1975, 18 (11): 613- 620.
doi: 10.1145/361219.361220 |
| 60 | LI D , BISSYANDÉ T F , KLEIN J , et al. Time series classification with discrete wavelet transformed data[J]. International Journal of Software Engineering and Knowledge Engineering, 2016, 26 (10): 1361- 1377. |
| 61 |
SALVADOR S , CHAN P . Toward accurate dynamic time warping in linear time and space[J]. Intelligent Data Analysis, 2007, 11 (5): 561- 580.
doi: 10.3233/IDA-2007-11508 |
| 62 |
TAUBMAN D S , MARCELLIN M W . JPEG2000: standard for interactive imaging[J]. Proceedings of the IEEE, 2002, 90 (8): 1336- 1357.
doi: 10.1109/JPROC.2002.800725 |
| 63 | ZHANG H, HO T B, LIN M S. A non-parametric wavelet feature extractor for time series classification[C]//Proc. of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2004: 595-603. |
| 64 | POPIVANOV I, MILLER R J. Similarity search over time-series data using wavelets[C]//Proc. of the International Confe-rence on Data Engineering, 2002: 212-221. |
| 65 | LI Q F, SHEN L L, GUO S, et al. Wavelet integrated CNNs for noise-robust image classification[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 7245-7254. |
| 66 | YUAN B, WANG C, JIANG F, et al. WaveletFCNN: a deep time series classification model for wind turbine blade icing detection[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/1902.05625.vl.pdf. |
| 67 | WANG J Y, WANG Z, LI F J, et al. Multilevel wavelet decomposition network for interpretable time series analysis[C]//Proc. of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018: 2437-2446. |
| 68 |
BAGNALL A , JANACEK G . A run length transformation for discriminating between auto regressive time series[J]. Journal of Classification, 2014, 31 (2): 154- 178.
doi: 10.1007/s00357-013-9135-6 |
| 69 |
CAIADO J , CRATO N , PEÑA D . A periodogram-based metric for time series classification[J]. Computational Statistics and Data Analysis, 2006, 50 (10): 2668- 2684.
doi: 10.1016/j.csda.2005.04.012 |
| 70 | BAGNALL A, DAVIS L, HILLS J, et al. Transformation based ensembles for time series classification[C]//Proc. of the SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, 2012: 307-318. |
| 71 | WANG Z, OATES T. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks[C]//Proc. of the Workshops at the 29th AAAI Conference on Artificial Intelligence, 2015. |
| 72 | WANG Z, OATES T. Imaging time-series to improve classification and imputation[C]//Proc. of the 24th International Joint Conference on Artificial Intelligence, 2015: 3939-3945. |
| 73 | CAMPANHARO A S L O , SIRER M I , MALMGREN R D , et al. Duality between time series and networks[J]. PloS One, 2011, 6 (8): 378- 390. |
| 74 | NGIAM J Q , CHEN Z H , CHIA D , et al. Tiled convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2010, 23, 1279- 1287. |
| 75 | ECKMANN J P , KAMPHORST S O , RUELLE D . Recurrence plots of dynamical systems[J]. World Scientific Series on Nonlinear Science Series, 1987, 4 (9): 973- 977. |
| 76 | SILVA D F, DE SOUZA V M A, BATISTA G E. Time series classification using compression distance of recurrence plots[C]//Proc. of the IEEE 13th International Conference on Data Mining, 2013: 687-696. |
| 77 |
HATAMI N , GAVET Y , DEBAYLE J . Bag of recurrence patterns representation for time-series classification[J]. Pattern Analysis and Applications, 2019, 22 (3): 877- 887.
doi: 10.1007/s10044-018-0703-6 |
| 78 | HATAMI N, GAVET Y, DEBAYLE J. Classification of time-series images using deep convolutional neural networks[C]//Proc. of the 10th International Society for Optics and Photonics, 2018. |
| 79 |
ZHANG Y , HOU Y , ZHOU S L , et al. Encoding time series as multi-scale signed recurrence plots for classification using fully con-volutional networks[J]. Sensors, 2020, 20 (14): 3818.
doi: 10.3390/s20143818 |
| 80 |
CAMPANA B J L , KEOGH E J . A compression-based distance measure for texture[J]. Statistical Analysis and Data Mining: the ASA Data Science Journal, 2010, 3 (6): 381- 398.
doi: 10.1002/sam.10093 |
| 81 | WANG J J, YANG J C, YU K, et al. Locality-constrained linear coding for image classification[C]//Proc. of the IEEE 23rd Conference on Computer Vision & Pattern Recognition, 2010: 3360-3367. |
| 82 |
BAGNALL A , LINES J , HILLS J , et al. Time-series classification with COTE: the collective of transformation-based ensembles[J]. IEEE Trans.on Knowledge and Data Engineering, 2015, 27 (9): 2522- 2535.
doi: 10.1109/TKDE.2015.2416723 |
| 83 | CORDUAS M , PICCOLO D . Time series clustering and classification by the autoregressive metric[J]. Computational Statistics & Data Analysis, 2008, 52 (4): 1860- 1872. |
| 84 | SIMPKINS A . System identification: theory for the user[J]. IEEE Robotics & Automation Magazine, 2012, 19 (2): 95- 96. |
| 85 |
CARDEN E P , BROWNJOHN J M W . ARMA modelled time-series classification for structural health monitoring of civil infrastructure[J]. Mechanical Systems and Signal Processing, 2008, 22 (2): 295- 314.
doi: 10.1016/j.ymssp.2007.07.003 |
| 86 |
BAYDOGAN M G , RUNGER G . Time series representation and similarity based on local autopatterns[J]. Data Mining and Knowledge Discovery, 2016, 30 (2): 476- 509.
doi: 10.1007/s10618-015-0425-y |
| 87 |
HVSKEN M , STAGGE P . Recurrent neural networks for time series classification[J]. Neurocomputing, 2003, 50, 223- 235.
doi: 10.1016/S0925-2312(01)00706-8 |
| 88 |
HOCHREITER S , SCHMIDHUBER J . Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780.
doi: 10.1162/neco.1997.9.8.1735 |
| 89 | TANISARO P, HEIDEMANN G. Time series classification using time warping invariant echo state networks[C]//Proc. of the IEEE 15th International Conference on Machine Learning and Applications, 2016: 831-836. |
| 90 | PASCANU R, GULCEHRE C, CHO K, et al. How to construct deep recurrent neural networks[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/1312.6026.pdf. |
| 91 | JAEGER H . Adaptive nonlinear system identification with echo state networks[J]. Advances in Neural Information Processing Systems, 2002, 15, 609- 616. |
| 92 | LINES J , TAYLOR S , BAGNALL A . Time series classification with HIVE-COTE: the hierarchical vote collective of transformation-based ensembles[J]. ACM Transactions on Knowledge Discovery from Data, 2018, 12 (5): 52- 73. |
| 93 |
SHIFAZ A , PELLETIER C , PETITJEAN F , et al. TS-chief: a scalable and accurate forest algorithm for time series classification[J]. Data Mining and Knowledge Discovery, 2020, 34 (3): 742- 775.
doi: 10.1007/s10618-020-00679-8 |
| 94 | 葛轶洲, 许翔, 杨锁荣, 等. 序列数据的数据增强方法综述[J]. 计算机科学与探索, 2021, 15 (7): 1207- 1219. |
| GE Y Z , XU X , YANG S R , et al. A survey on sequence data augmentation[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15 (7): 1207- 1219. | |
| 95 | LE GUENNEC A, MALINOWSKI S, TAVENARD R. Data augmentation for time series classification using convolutional neural networks[C]//Proc. of the ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, 2016. |
| 96 | FIELDS T, HSIEH G, CHENOU J. Mitigating drift in time series data with noise augmentation[C]//Proc. of the IEEE International Conference on Computational Science and Computational Intelligence, 2019: 227-230. |
| 97 | GAO J K, SONG X M, WEN Q S, et al. RobustTAD: robust time series anomaly detection via decomposition and convolutional neural networks[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/2002.09545.pdf. |
| 98 |
STEVEN E O , HAN D S . Feature representation and data augmentation for human activity classification based on wearable IMU sensor data using a deep LSTM neural network[J]. Sensors, 2018, 18 (9): 2892.
doi: 10.3390/s18092892 |
| 99 | IWANA B K , UCHIDA S . An empirical survey of data augmentation for time series classification with neural networks[J]. PloS One, 2021, 16 (7): e0254841. |
| 100 | CHAWLA N V , BOWYER K W , HALL L O , et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16 (1): 321- 357. |
| 101 | FAWAZ H I, FORESTIER G, WEBER J, et al. Data augmentation using synthetic data for time series classification with deep residual networks[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/1808.02455.pdf. |
| 102 | ESTEBAN C, HYLAND S L, RÄTSCH G. Real-valued (medical) time series generation with recurrent conditional gans[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/1706.02633.pdf. |
| 103 | ZHU F , YE F , FU Y C , et al. Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network[J]. Scientific Reports, 2019, 9 (1): 6734. |
| 104 | YOON J, JARRETT D, SCHAAR M V D. Time-series gene-rative adversarial networks[C]//Proc. of the 33rd Conference on Neural Information Processing Systems, 2019. |
| 105 | WEN Q S, SUN L, YANG F, et al. Time series data augmentation for deep learning: a survey[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/2002.12478.pdf. |
| 106 | TAYLOR L, NITSCHKE G. Improving deep learning with generic data augmentation[C]//Proc. of the IEEE Symposium Series on Computational Intelligence, 2018: 1542-1547. |
| 107 | KASHIPAREKH K, NARWARIYA J, MALHOTRA P, et al. ConvTimeNet: a pre-trained deep convolutional neural network for time series classification[C]//Proc. of the IEEE International Joint Conference on Neural Networks, 2019. |
| 108 | FAWAZ H I, FORESTIER G, WEBER J, et al. Transfer learning for time series classification[C]//Proc. of the IEEE International Conference on Big Data, 2018: 1367-1376. |
| 109 | FRIEDMAN M . A comparison of alternative tests of significance for the problem of m rankings[J]. The Annals of Mathematical Statistics, 1940, 11 (1): 86- 92. |
| 110 | BENAVOLI A , CORANI G , MANGILI F . Should we really use post-hoc tests based on mean-ranks?[J]. The Journal of Machine Learning Research, 2016, 17 (1): 152- 161. |
| 111 | DEMŠAR J . Statistical comparisons of classifiers over multiple data sets[J]. The Journal of Machine Learning Research, 2006, 7 (1): 1- 30. |
| 112 | KINGMA D P, BA J. Adam: a method for stochastic optimization[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/1412.6980.pdf. |
| 113 | ITAKURA F . Minimum prediction residual principle applied to speech recognition[J]. IEEE Trans.on Acoustics, Speech, and Signal Processing, 1975, 23 (1): 67- 72. |
| 114 | 翟婷婷, 高阳, 朱俊武. 面向流数据分类的在线学习综述[J]. 软件学报, 2020, 31 (4): 912- 931. |
| ZHAI T T , GAO Y , ZHU J W . Survey of online learning algorithms for streaming data classification[J]. Journal of Software, 2020, 31 (4): 912- 931. | |
| 115 | 郭虎升, 张爱娟, 王文剑. 基于在线性能测试的概念漂移检测方法[J]. 软件学报, 2020, 31 (4): 932- 947. |
| GUO H S , ZHANG A J , WANG W J . Concept drift detection method based on online performance test[J]. Journal of Software, 2020, 31 (4): 932- 947. | |
| 116 | 杜诗语, 韩萌, 申明尧, 等. 概念漂移数据流集成分类算法综述[J]. 计算机工程, 2020, 46 (1): 15- 24. |
| DU S Y , HAN M , SHEN M Y , et al. Survey of ensemble classification algorithms for data streams with concept drift[J]. Computer Engineering, 2020, 46 (1): 15- 24. | |
| 117 | LAN Z, CHEN M, GOODMAN S, et al. Albert: a lite bert for self-supervised learning of language representations[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/1909.11942.pdf. |
| 118 | DEVLIN J, CHANG M W, LEE K, et al. Bert: pre-training of deep bidirectional transformers for language understanding[EB/OL]. [2021-03-16]. https://arxiv.org/pdf/1810.04805.pdf. |
| 119 | HUANG S, XIONG Y, ZHANG Y, et al. Unsupervised triplet hashing for fast image retrieval[C]//Proc. of the on Thematic Workshops of ACM Multimedia, 2017: 84-92. |
| 120 | CHEN X L, XIE S N, HE K M. An empirical study of training self-supervised visual transformers[C]//Proc. of the IEEE/CVF International Conference on Computer Vision, 2021: 9640-9649. |
| [1] | Ruize LI, Shuanghui ZHANG, Yongxiang LIU. Computational efficient structural sparse ISAR imaging method based on convolutional ADMM-net [J]. Systems Engineering and Electronics, 2023, 45(1): 56-70. |
| [2] | Xiao HAN, Shiwen CHEN, Meng CHEN, Jincheng YANG. Open-set recognition of LPI radar signal based on reciprocal point learning [J]. Systems Engineering and Electronics, 2022, 44(9): 2752-2759. |
| [3] | Limin ZHANG, Kaiwen TAN, Wenjun YAN, Yuyuan ZHANG. Radar emitter recognition based on multi-level jumper residual network [J]. Systems Engineering and Electronics, 2022, 44(7): 2148-2156. |
| [4] | Guodong JIN, Yuanliang XUE, Lining TAN, Jiankun XU. Advances in object tracking algorithm based on siamese network [J]. Systems Engineering and Electronics, 2022, 44(6): 1805-1822. |
| [5] | Xiaofeng ZHAO, Yebin XU, Fei WU, Jiahui NIU, Wei CAI, Zhili ZHANG. Ground infrared target detection method based on global sensing mechanism [J]. Systems Engineering and Electronics, 2022, 44(5): 1461-1467. |
| [6] | Hong ZOU, Chenyang BAI, Peng HE, Yaping CUI, Ruyan WANG, Dapeng WU. Edge service placement strategy based on distributed deep learning [J]. Systems Engineering and Electronics, 2022, 44(5): 1728-1737. |
| [7] | Lin HUANG, Li GONG, Wei JIANG, Kangbo WANG. Remaining useful life prediction based on multi-source information fusion and HMM [J]. Systems Engineering and Electronics, 2022, 44(5): 1747-1756. |
| [8] | Dong CHEN, Yanwei JU. Ship object detection SAR images based on semantic segmentation [J]. Systems Engineering and Electronics, 2022, 44(4): 1195-1201. |
| [9] | Jingming SUN, Shengkang YU, Jun SUN. Pose sensitivity analysis of HRRP recognition based on deep learning [J]. Systems Engineering and Electronics, 2022, 44(3): 802-807. |
| [10] | Yunxiang YAO, Ying CHEN. Target tracking network based on dual-modal interactive fusion under attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(2): 410-419. |
| [11] | Chunling XUE, Fei CAO, Qing SUN, Jianqiang QIN, Xiaowei FENG. Sea-surface weak target detection based on multi-feature information fusion [J]. Systems Engineering and Electronics, 2022, 44(11): 3338-3345. |
| [12] | Yongxing GAO, Xudong WANG, Ling WANG, Daiyin ZHU, Jun GUO, Fanwang MENG. Weather signal detection for dual polarization weather radar based on RCNN [J]. Systems Engineering and Electronics, 2022, 44(11): 3380-3387. |
| [13] | Yu ZHANG, Kai WU, Jie GUO, Zhishan GE, Baochao ZHANG. Adaptive sequential track-association algorithm based on data quality assessment [J]. Systems Engineering and Electronics, 2022, 44(11): 3477-3485. |
| [14] | Yiheng ZHOU, Jun YANG, Saiqiang XIA, Mingjiu LYU. Estimation method of micro-motion parameters for rotor targets under flashing [J]. Systems Engineering and Electronics, 2022, 44(1): 54-63. |
| [15] | Rong FU, Tianyao HUANG, Yimin LIU. DNN based 1-bit block sparse recovery in frequency agile coherent radar [J]. Systems Engineering and Electronics, 2022, 44(1): 70-75. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||