| 基于加权局部复杂不变性的时间序列分类算法 |
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| 引用本文： | 李怡桐,刘晓涛,刘静,吴凯.基于加权局部复杂不变性的时间序列分类算法[J].系统仿真学报,2022,34(10):2194-2203. |
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| 作者姓名： | 李怡桐 刘晓涛 刘静 吴凯 |
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| 作者单位： | 1.西安电子科技大学 广州研究院，广东 广州 5105552.西安电子科技大学 人工智能学院，陕西 西安 710071 |
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| 基金项目： | 国家自然科学基金(61773300);科技部科技创新2030 -“新一代人工智能”重大项目(2018AAA0101302) |
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| 摘 要： | 为解决现有方法对较长、复杂度分布不均序列的错分类问题，提取序列复杂度的局部信息，提出了加权局部复杂度不变性距离（WLCID），包含复杂度局部表征和全局加权整合两个模型。利用滑窗分解序列，结合复杂度不变性距离表示方法提取局部复杂度信息；通过建立类表征模型，以类间距越大的子段对分类正确的贡献度越大为依据，通过归一化累积类间距来量化整合权重。与相似算法的对比实验表明：此方法不仅在复杂度分布不均的数据中表现突出，在大多数测试集也有较好的效果。在分类和聚类任务上精度的提升，说明方法在表示时间序列形态特征的复杂度信息上具有较好的能力。
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| 关 键 词： | 复杂度不变性距离 局部复杂度表征 全局复杂度加权整合 类表征模型 时间序列分类 |
| 收稿时间： | 2021-05-21 |
Weighted Local Complexity Invariance for Time Series Classification |
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| Yitong Li,Xiaotao Liu,Jing Liu,Kai Wu.Weighted Local Complexity Invariance for Time Series Classification[J].Journal of System Simulation,2022,34(10):2194-2203. |
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| Authors: | Yitong Li Xiaotao Liu Jing Liu Kai Wu |
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| Affiliation: | 1.Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China2.Department of Artificial Intelligence, Xidian University, Xi'an 710071, China |
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| Abstract: | Aiming at the misclassification of existing algorithms for long or unevenly distributed time series, the local complexity information is extracted and weighted local complexity-invariant distance (WLCID) is proposed, which includes the local complexity representation model and the weighted global complexity integration model. Sliding window is used to split up time series, and combined with the complexity-invariant distance, the local complexity information can be extracted. As to the class representation model, the integration weights are quantified with the normalized cumulative between-class distance, with the perspective that the subsequence contributes more greatly with larger between-class distance. Compared with other similar algorithms, the proposed method is good at dealing with the data with uneven complexity distribution and can also performs better in most of the test datasets processing. Besides classification tasks, the improvement in the accuracy of clustering tasks also shows its ability to represent the complexity information of the morphological characteristics of time series. |
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| Keywords: | complexity-invariant distance local complexity representation weighted global complexity integration class representation model time series classification |
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