Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2660-2666.DOI: 10.11772/j.issn.1001-9081.2023091278
• Artificial intelligence • Previous Articles Next Articles
Received:
2023-09-20
Revised:
2024-03-13
Accepted:
2024-03-21
Online:
2024-04-16
Published:
2024-09-10
Contact:
Qinzhuang ZHAO
About author:
TAN Hongye, born in 1971, Ph. D., professor. Her research interests include natural language processing.
Supported by:
通讯作者:
赵秦壮
作者简介:
赵秦壮(1998—),男,山西运城人,博士研究生,CCF会员,主要研究方向:因果推断基金资助:
CLC Number:
Qinzhuang ZHAO, Hongye TAN. Time series causal inference method based on adaptive threshold learning[J]. Journal of Computer Applications, 2024, 44(9): 2660-2666.
赵秦壮, 谭红叶. 基于自适应阈值学习的时序因果推断方法[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2660-2666.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023091278
算法 | α | 数据集a | 数据集b | 数据集c | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Pre/% | Rec/% | F1/% | Pre/% | Rec/% | F1/% | Pre/% | Rec/% | F1/% | |||
自适应PC算法 | 0.20 | 80.82 | 82.58 | 81.69 | 68.91 | 75.12 | 71.84 | 69.03 | 64.81 | 66.83 | |
0.15 | 81.71 | 81.80 | 81.75 | 69.77 | 74.18 | 71.87 | 69.39 | 64.31 | 66.75 | ||
0.10 | 81.34 | 80.58 | 80.96 | 70.73 | 73.07 | 71.85 | 70.22 | 64.13 | 67.02 | ||
0.05 | 81.66 | 79.48 | 80.56 | 71.97 | 71.79 | 71.86 | 70.65 | 63.50 | 66.88 | ||
0.01 | 77.78 | 75.15 | 76.44 | 71.43 | 68.93 | 70.14 | 70.08 | 62.44 | 66.03 | ||
阈值初始化+ 阈值调整 | 82.09 | 81.64 | 81.87 | 70.46 | 74.50 | 72.39 | 69.29 | 65.06 | 67.09 | ||
自适应PCMCI算法 | 第1阶段 | 0.20 | 68.09 | 95.10 | 79.36 | 55.53 | 95.00 | 70.09 | 58.27 | 98.26 | 73.16 |
0.10 | 75.93 | 93.99 | 84.00 | 68.39 | 93.86 | 79.13 | 71.42 | 95.78 | 81.83 | ||
0.05 | 79.42 | 92.77 | 85.58 | 76.31 | 92.96 | 83.82 | 79.53 | 95.37 | 86.73 | ||
0.01 | 83.28 | 90.97 | 86.96 | 85.64 | 91.54 | 88.49 | 87.42 | 94.03 | 90.61 | ||
阈值初始化 | 79.41 | 93.41 | 85.85 | 81.99 | 92.37 | 86.87 | 77.48 | 95.81 | 85.67 | ||
阈值调整 | 87.60 | 95.71 | 91.48 | 84.77 | 93.52 | 88.93 | 61.97 | 96.86 | 75.58 | ||
第2阶段 | 0.20 | 72.07 | 100.00 | 83.77 | 71.96 | 99.36 | 83.47 | 39.68 | 100.00 | 56.82 | |
0.10 | 70.84 | 100.00 | 82.93 | 69.01 | 99.40 | 81.46 | 39.20 | 100.00 | 56.32 | ||
0.05 | 70.21 | 100.00 | 82.50 | 66.33 | 99.44 | 79.58 | 38.72 | 100.00 | 55.83 | ||
0.01 | 69.13 | 100.00 | 81.74 | 62.55 | 99.51 | 76.82 | 37.79 | 100.00 | 54.86 | ||
阈值初始化 | 71.00 | 100.00 | 83.04 | 72.73 | 99.72 | 84.11 | 40.81 | 100.00 | 57.96 | ||
阈值调整 | 73.57 | 100.00 | 84.77 | 73.35 | 100.00 | 84.63 | 43.29 | 99.76 | 60.38 | ||
自适应VAR-LINGAM算法 | 0.40 | 100.00 | 79.01 | 88.28 | 99.99 | 84.87 | 91.81 | 99.31 | 78.53 | 87.71 | |
0.30 | 100.00 | 93.30 | 96.53 | 99.81 | 89.97 | 94.63 | 90.48 | 88.58 | 89.52 | ||
0.20 | 99.99 | 99.96 | 99.97 | 98.02 | 94.84 | 96.41 | 56.53 | 94.74 | 70.81 | ||
0.15 | 97.47 | 100.00 | 98.72 | 95.55 | 98.57 | 97.04 | 34.74 | 97.53 | 51.24 | ||
0.10 | 94.54 | 100.00 | 97.19 | 86.36 | 99.80 | 92.59 | 19.88 | 99.26 | 33.12 | ||
阈值初始化 | 100.00 | 100.00 | 100.00 | 99.55 | 91.00 | 95.08 | 93.98 | 87.26 | 90.50 | ||
阈值调整 | — | — | — | 99.42 | 99.39 | 99.40 | 92.45 | 93.34 | 92.89 |
Tab. 1 Comparison of experimental results of three classical algorithms before and after improvement
算法 | α | 数据集a | 数据集b | 数据集c | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Pre/% | Rec/% | F1/% | Pre/% | Rec/% | F1/% | Pre/% | Rec/% | F1/% | |||
自适应PC算法 | 0.20 | 80.82 | 82.58 | 81.69 | 68.91 | 75.12 | 71.84 | 69.03 | 64.81 | 66.83 | |
0.15 | 81.71 | 81.80 | 81.75 | 69.77 | 74.18 | 71.87 | 69.39 | 64.31 | 66.75 | ||
0.10 | 81.34 | 80.58 | 80.96 | 70.73 | 73.07 | 71.85 | 70.22 | 64.13 | 67.02 | ||
0.05 | 81.66 | 79.48 | 80.56 | 71.97 | 71.79 | 71.86 | 70.65 | 63.50 | 66.88 | ||
0.01 | 77.78 | 75.15 | 76.44 | 71.43 | 68.93 | 70.14 | 70.08 | 62.44 | 66.03 | ||
阈值初始化+ 阈值调整 | 82.09 | 81.64 | 81.87 | 70.46 | 74.50 | 72.39 | 69.29 | 65.06 | 67.09 | ||
自适应PCMCI算法 | 第1阶段 | 0.20 | 68.09 | 95.10 | 79.36 | 55.53 | 95.00 | 70.09 | 58.27 | 98.26 | 73.16 |
0.10 | 75.93 | 93.99 | 84.00 | 68.39 | 93.86 | 79.13 | 71.42 | 95.78 | 81.83 | ||
0.05 | 79.42 | 92.77 | 85.58 | 76.31 | 92.96 | 83.82 | 79.53 | 95.37 | 86.73 | ||
0.01 | 83.28 | 90.97 | 86.96 | 85.64 | 91.54 | 88.49 | 87.42 | 94.03 | 90.61 | ||
阈值初始化 | 79.41 | 93.41 | 85.85 | 81.99 | 92.37 | 86.87 | 77.48 | 95.81 | 85.67 | ||
阈值调整 | 87.60 | 95.71 | 91.48 | 84.77 | 93.52 | 88.93 | 61.97 | 96.86 | 75.58 | ||
第2阶段 | 0.20 | 72.07 | 100.00 | 83.77 | 71.96 | 99.36 | 83.47 | 39.68 | 100.00 | 56.82 | |
0.10 | 70.84 | 100.00 | 82.93 | 69.01 | 99.40 | 81.46 | 39.20 | 100.00 | 56.32 | ||
0.05 | 70.21 | 100.00 | 82.50 | 66.33 | 99.44 | 79.58 | 38.72 | 100.00 | 55.83 | ||
0.01 | 69.13 | 100.00 | 81.74 | 62.55 | 99.51 | 76.82 | 37.79 | 100.00 | 54.86 | ||
阈值初始化 | 71.00 | 100.00 | 83.04 | 72.73 | 99.72 | 84.11 | 40.81 | 100.00 | 57.96 | ||
阈值调整 | 73.57 | 100.00 | 84.77 | 73.35 | 100.00 | 84.63 | 43.29 | 99.76 | 60.38 | ||
自适应VAR-LINGAM算法 | 0.40 | 100.00 | 79.01 | 88.28 | 99.99 | 84.87 | 91.81 | 99.31 | 78.53 | 87.71 | |
0.30 | 100.00 | 93.30 | 96.53 | 99.81 | 89.97 | 94.63 | 90.48 | 88.58 | 89.52 | ||
0.20 | 99.99 | 99.96 | 99.97 | 98.02 | 94.84 | 96.41 | 56.53 | 94.74 | 70.81 | ||
0.15 | 97.47 | 100.00 | 98.72 | 95.55 | 98.57 | 97.04 | 34.74 | 97.53 | 51.24 | ||
0.10 | 94.54 | 100.00 | 97.19 | 86.36 | 99.80 | 92.59 | 19.88 | 99.26 | 33.12 | ||
阈值初始化 | 100.00 | 100.00 | 100.00 | 99.55 | 91.00 | 95.08 | 93.98 | 87.26 | 90.50 | ||
阈值调整 | — | — | — | 99.42 | 99.39 | 99.40 | 92.45 | 93.34 | 92.89 |
延迟 | 步骤 | 数据组1 | 数据组2 | 数据组3 | 数据组4 | |
---|---|---|---|---|---|---|
0 | 平均相关系数 计算(r) | 0.509 4 | 0.458 0 | 0.522 6 | 0.556 6 | |
1 | 0.495 7 | 0.441 3 | 0.510 6 | 0.542 7 | ||
2 | 0.482 6 | 0.426 5 | 0.501 1 | 0.531 6 | ||
3 | 0.469 4 | 0.409 3 | 0.491 8 | 0.518 9 | ||
0 | 阈值初始化(α) | 0.095 1 | 0.094 6 | 0.095 2 | 0.095 6 | |
1 | 0.067 5 | 0.066 9 | 0.064 0 | 0.065 9 | ||
2 | 0.041 2 | 0.042 5 | 0.039 1 | 0.042 3 | ||
3 | 0.014 7 | 0.014 1 | 0.014 9 | 0.015 2 | ||
0 | 数量 估计 | n=0 | 0 | 0 | 0 | 0 |
1 | n=5 | 5 | 6 | 5 | 5 | |
2 | n=1 | 1 | 0 | 1 | 1 | |
3 | n=1 | 0 | 0 | 0 | 1 | |
0 | 最终阈值(α) | 0.001 0 | 0.061 3 | 0.001 0 | 0.001 9 | |
1 | 0.083 0 | 0.101 4 | 0.074 6 | 0.063 3 | ||
2 | 0.031 4 | 0.003 2 | 0.041 2 | 0.051 7 | ||
3 | 0.001 0 | 0.001 0 | 0.001 0 | 0.023 2 |
Tab. 2 Experimental parameters of PCMCI algorithm
延迟 | 步骤 | 数据组1 | 数据组2 | 数据组3 | 数据组4 | |
---|---|---|---|---|---|---|
0 | 平均相关系数 计算(r) | 0.509 4 | 0.458 0 | 0.522 6 | 0.556 6 | |
1 | 0.495 7 | 0.441 3 | 0.510 6 | 0.542 7 | ||
2 | 0.482 6 | 0.426 5 | 0.501 1 | 0.531 6 | ||
3 | 0.469 4 | 0.409 3 | 0.491 8 | 0.518 9 | ||
0 | 阈值初始化(α) | 0.095 1 | 0.094 6 | 0.095 2 | 0.095 6 | |
1 | 0.067 5 | 0.066 9 | 0.064 0 | 0.065 9 | ||
2 | 0.041 2 | 0.042 5 | 0.039 1 | 0.042 3 | ||
3 | 0.014 7 | 0.014 1 | 0.014 9 | 0.015 2 | ||
0 | 数量 估计 | n=0 | 0 | 0 | 0 | 0 |
1 | n=5 | 5 | 6 | 5 | 5 | |
2 | n=1 | 1 | 0 | 1 | 1 | |
3 | n=1 | 0 | 0 | 0 | 1 | |
0 | 最终阈值(α) | 0.001 0 | 0.061 3 | 0.001 0 | 0.001 9 | |
1 | 0.083 0 | 0.101 4 | 0.074 6 | 0.063 3 | ||
2 | 0.031 4 | 0.003 2 | 0.041 2 | 0.051 7 | ||
3 | 0.001 0 | 0.001 0 | 0.001 0 | 0.023 2 |
延迟 | 步骤 | 数据组1 | 数据组2 | 数据组3 | 数据组4 | |
---|---|---|---|---|---|---|
0 | 阈值 初始化(α) | 0.340 0 | 0.340 0 | 0.340 0 | 0.340 0 | |
1 | 0.336 8 | 0.335 3 | 0.334 0 | 0.336 7 | ||
2 | 0.332 5 | 0.330 2 | 0.329 7 | 0.332 0 | ||
3 | 0.327 4 | 0.324 2 | 0.322 8 | 0.325 9 | ||
4 | 0.319 5 | 0.317 5 | 0.314 7 | 0.318 9 | ||
5 | 0.310 6 | 0.309 8 | 0.307 2 | 0.310 0 | ||
0 | 数 量 估 计 | n=0 | 0 | 0 | 0 | 0 |
1 | n=12 | 14 | 10 | 11 | 13 | |
2 | n=2 | 1 | 3 | 2 | 3 | |
3 | n=3 | 3 | 3 | 3 | 2 | |
4 | n=1 | 1 | 1 | 1 | 1 | |
5 | n=1 | 0 | 3 | 1 | 3 | |
0 | 最终 阈值(α) | 0.340 0 | 0.340 0 | 0.340 0 | 0.340 0 | |
1 | 0.212 4 | 0.723 5 | 0.692 5 | 0.184 1 | ||
2 | 0.466 5 | 0.289 7 | 0.329 7 | 0.316 0 | ||
3 | 0.267 5 | 0.324 2 | 0.318 8 | 0.325 9 | ||
4 | 0.238 5 | 0.317 5 | 0.314 7 | 0.318 9 | ||
5 | 0.310 6 | 0.216 3 | 0.307 2 | 0.210 8 |
Tab. 3 Experimental parameters of VAR-LINGAM algorithm
延迟 | 步骤 | 数据组1 | 数据组2 | 数据组3 | 数据组4 | |
---|---|---|---|---|---|---|
0 | 阈值 初始化(α) | 0.340 0 | 0.340 0 | 0.340 0 | 0.340 0 | |
1 | 0.336 8 | 0.335 3 | 0.334 0 | 0.336 7 | ||
2 | 0.332 5 | 0.330 2 | 0.329 7 | 0.332 0 | ||
3 | 0.327 4 | 0.324 2 | 0.322 8 | 0.325 9 | ||
4 | 0.319 5 | 0.317 5 | 0.314 7 | 0.318 9 | ||
5 | 0.310 6 | 0.309 8 | 0.307 2 | 0.310 0 | ||
0 | 数 量 估 计 | n=0 | 0 | 0 | 0 | 0 |
1 | n=12 | 14 | 10 | 11 | 13 | |
2 | n=2 | 1 | 3 | 2 | 3 | |
3 | n=3 | 3 | 3 | 3 | 2 | |
4 | n=1 | 1 | 1 | 1 | 1 | |
5 | n=1 | 0 | 3 | 1 | 3 | |
0 | 最终 阈值(α) | 0.340 0 | 0.340 0 | 0.340 0 | 0.340 0 | |
1 | 0.212 4 | 0.723 5 | 0.692 5 | 0.184 1 | ||
2 | 0.466 5 | 0.289 7 | 0.329 7 | 0.316 0 | ||
3 | 0.267 5 | 0.324 2 | 0.318 8 | 0.325 9 | ||
4 | 0.238 5 | 0.317 5 | 0.314 7 | 0.318 9 | ||
5 | 0.310 6 | 0.216 3 | 0.307 2 | 0.210 8 |
算法 | 模块 | Pre | Rec | F1 |
---|---|---|---|---|
PCMCI | 原算法+自适应阈值 | 41.70 | 98.42 | 58.58 |
原算法+阈值调整 | 41.41 | 98.42 | 58.29 | |
原算法+阈值初始化 | 40.47 | 98.01 | 57.29 | |
原算法 | 40.38 | 97.90 | 57.17 | |
VAR-LINGAM | 原算法+自适应阈值 | 98.45 | 98.45 | 98.45 |
原算法+阈值调整 | 98.44 | 98.43 | 98.43 | |
原算法+阈值初始化 | 98.60 | 92.44 | 95.42 | |
原算法 | 99.05 | 90.91 | 94.80 |
Tab. 4 Ablation experimental results
算法 | 模块 | Pre | Rec | F1 |
---|---|---|---|---|
PCMCI | 原算法+自适应阈值 | 41.70 | 98.42 | 58.58 |
原算法+阈值调整 | 41.41 | 98.42 | 58.29 | |
原算法+阈值初始化 | 40.47 | 98.01 | 57.29 | |
原算法 | 40.38 | 97.90 | 57.17 | |
VAR-LINGAM | 原算法+自适应阈值 | 98.45 | 98.45 | 98.45 |
原算法+阈值调整 | 98.44 | 98.43 | 98.43 | |
原算法+阈值初始化 | 98.60 | 92.44 | 95.42 | |
原算法 | 99.05 | 90.91 | 94.80 |
1 | BISWAS R, SHLIZERMAN E. Statistical perspective on functional and causal neural connectomics: a comparative study [J]. Frontiers in Systems Neuroscience, 2022, 16: 817962. |
2 | MIERSCH P, JIANG S, RAKOVEC O, et al. Identifying drivers of river floods using causal inference [C]// Proceedings of the 25th European Geosciences Union General Assembly. [S.l.]: EGU Press & Media, 2023: EG U23-12948. |
3 | LUO D, LIAO W, LI S, et al. Causality-guided multi-memory interaction network for multivariate stock price movement prediction [C]// Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg: ACL, 2023: 12164-12176. |
4 | RUNGE J, BATHIANY S, BOLLT E, et al. Inferring causation from time series in Earth system sciences [J]. Nature Communications, 2019, 10: 2553. |
5 | ASSAAD C K, DEVIJVER E, GAUSSIER E. Survey and evaluation of causal discovery methods for time series [J]. Journal of Artificial Intelligence Research, 2022, 73: 767-819. |
6 | RUNGE J, NOWACK P, KRETSCHMER M, et al. Detecting and quantifying causal associations in large nonlinear time series datasets[J]. Science Advances, 2019, 5(11): eaau4996. |
7 | HYVÄRINEN A, SHIMIZU S, HOYER P O. Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity [C]// Proceedings of the 25th International Conference on Machine Learning. New York: ACM, 2008: 424-431. |
8 | STROBL E V. Automated hyperparameter selection for the PC algorithm [J]. Pattern Recognition Letters, 2021, 151: 288-293. |
9 | SCHWARZ G. Estimating the dimension of a model [J]. The Annals of Statistics, 1978, 6(2): 461-464. |
10 | RAGHU V K, POON A, BENOS P V. Evaluation of causal structure learning methods on mixed data types [J]. Proceedings of Machine Learning Research, 2018, 92: 48-65. |
11 | BIZA K, TSAMARDINOS I, TRIANTAFILLOU S. Tuning causal discovery algorithms [C]// Proceedings of the 10th International Conference on Probabilistic Graphical Models. New York: JMLR.org, 2020: 17-28. |
12 | 郝志峰,吕宏伟,蔡瑞初,等.基于条件独立性的LiNGAM模型剪枝算法[J].计算机应用与软件,2016,33(8):249-253. |
HAO Z F, LYU H W, CAI R C, et al. LiNGAM model pruning algorithm based on conditional independence [J]. Computer Applications and Software, 2016, 33(8): 249-253. | |
13 | ASSAAD C K, BYSTROVA D, ARBEL J, et al. Hybrids of constraint-based and noise-based algorithms for causal discovery from time series [EB/OL]. [2023-10-02]. . |
14 | ASSAAD C K, DEVIJVER E, GAUSSIER E. Entropy-based discovery of summary causal graphs in time series [J]. Entropy, 2022, 24(8): 1156. |
15 | JAIN N, ZHANG D, AHMAD W U, et al. ContraCLM: contrastive learning for causal language model [C]// Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg: ACL, 2023: 6436-6459. |
16 | 曾泽凡,陈思雅,龙洗,等.基于观测数据的时间序列因果推断综述[J].大数据,2023,9(4):139-158. |
ZENG Z F, CHEN S Y, LONG X, et al. Overview of observational data-based time series causal inference [J]. Big Data Research, 2023,9(4):139-158. | |
17 | SPIRTES P, GLYMOUR C, SCHEINES R. Causation, Prediction, and Search [M]. Cambridge: MIT Press, 2000:84-90. |
18 | VERMA T S, PEARL J. Equivalence and synthesis of causal models [M]// Probabilistic and Causal Inference: The Works of Judea Pearl. New York: ACM, 2022: 221-236. |
19 | SHIMIZU S, HOYER P O, HYVÄRINEN A, et al. A linear non-Gaussian acyclic model for causal discovery [J]. Journal of Machine Learning Research, 2006, 7: 2003-2030. |
20 | ASSAAD C K, DEVIJVER E, GAUSSIER E, et al. A mixed noise and constraint-based approach to causal inference in time series [C]// Proceedings of the 2021 European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer, 2021: 453-468. |
21 | 郝志峰,张维杰,蔡瑞初,等.基于条件独立性检验的非稳态因果发现方法[J].计算机工程与应用,2024,60(10):113-120. |
HAO Z F, ZHANG W J, CAI R C, et al. Non-stationary causal discovery method based on conditional independence test [J]. Computer Engineering and Applications, 2024, 60(10):113-120. |
[1] | Lilin FAN, Fukang CAO, Wanting WANG, Kai YANG, Zhaoyu SONG. Intermittent demand forecasting method based on adaptive matching of demand patterns [J]. Journal of Computer Applications, 2024, 44(9): 2747-2755. |
[2] | Jiepo FANG, Chongben TAO. Hybrid internet of vehicles intrusion detection system for zero-day attacks [J]. Journal of Computer Applications, 2024, 44(9): 2763-2769. |
[3] | Liting LI, Bei HUA, Ruozhou HE, Kuang XU. Multivariate time series prediction model based on decoupled attention mechanism [J]. Journal of Computer Applications, 2024, 44(9): 2732-2738. |
[4] | Guanglei YAO, Juxia XIONG, Guowu YANG. Flower pollination algorithm based on neural network optimization [J]. Journal of Computer Applications, 2024, 44(9): 2829-2837. |
[5] | Jinjin LI, Guoming SANG, Yijia ZHANG. Multi-domain fake news detection model enhanced by APK-CNN and Transformer [J]. Journal of Computer Applications, 2024, 44(9): 2674-2682. |
[6] | Le YANG, Damin ZHANG, Qing HE, Jiaxin DENG, Fengqin ZUO. Application of improved hunter-prey optimization algorithm in WSN coverage [J]. Journal of Computer Applications, 2024, 44(8): 2506-2513. |
[7] | Hang XU, Zhi YANG, Xingyuan CHEN, Bing HAN, Xuehui DU. Coverage-guided fuzzing based on adaptive sensitive region mutation [J]. Journal of Computer Applications, 2024, 44(8): 2528-2535. |
[8] | Mei WANG, Xuesong SU, Jia LIU, Ruonan YIN, Shan HUANG. Time series classification method based on multi-scale cross-attention fusion in time-frequency domain [J]. Journal of Computer Applications, 2024, 44(6): 1842-1847. |
[9] | Yan LI, Dazhi PAN, Siqing ZHENG. Improved adaptive large neighborhood search algorithm for multi-depot vehicle routing problem with time window [J]. Journal of Computer Applications, 2024, 44(6): 1897-1904. |
[10] | Zixuan YUAN, Xiaoqing WENG, Ningzhen GE. Early classification model of multivariate time series based on orthogonal locality preserving projection and cost optimization [J]. Journal of Computer Applications, 2024, 44(6): 1832-1841. |
[11] | Jinfu WU, Yi LIU. Fast adversarial training method based on random noise and adaptive step size [J]. Journal of Computer Applications, 2024, 44(6): 1807-1815. |
[12] | Zexin XU, Lei YANG, Kangshun LI. Shorter long-sequence time series forecasting model [J]. Journal of Computer Applications, 2024, 44(6): 1824-1831. |
[13] | Hongtao SONG, Jiangsheng YU, Qilong HAN. Industrial multivariate time series data quality assessment method [J]. Journal of Computer Applications, 2024, 44(6): 1743-1750. |
[14] | Fan MENG, Qunli YANG, Jing HUO, Xinkuan WANG. EraseMTS: iterative active multivariable time series anomaly detection algorithm based on margin anomaly candidate set [J]. Journal of Computer Applications, 2024, 44(5): 1458-1463. |
[15] | Xiuxi WEI, Maosong PENG, Huajuan HUANG. Node coverage optimization of wireless sensor network based on multi-strategy improved butterfly optimization algorithm [J]. Journal of Computer Applications, 2024, 44(4): 1009-1017. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||