arXiv 2408.058492024-08-11cs.LGstat.ML

存在缺失值情况下的时间序列分类端到端模型

An End-to-End Model for Time Series Classification In the Presence of Missing Values

School of Computer Science and Engineering, Tianjin University of Technology（天津理工大学计算机科学与工程学院）；Technical University of Denmark（丹麦技术大学）；Aalborg University（奥尔堡大学）

Pengshuai Yao, Mengna Liu, Xu Cheng, Fan Shi, Huan Li, Xiufeng Liu, Shengyong Chen

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AI总结：

针对含缺失值的时间序列分类任务，提出一种统一插补与表征学习的端到端神经网络，以分类性能为核心，通过多尺度特征模块提取信息，在多类数据集上优于现有最优方法，高缺失率场景优势更明显。

摘要：

含缺失数据的时间序列分类是时间序列分析中的常见问题，因为实际应用中的时序数据往往包含缺失值。传统的两阶段方法将插补和分类分开处理，由于插补过程未利用标签信息，可能导致性能次优。另一方面，单阶段方法虽能在缺失信息下学习特征，但插补误差会在分类过程中传播，限制了特征表征能力。为克服这些挑战，本研究提出一种端到端神经网络，将数据插补与表征学习统一在单个框架内，使插补过程能够利用标签信息。与现有方法不同，我们的方法不过度关注插补数据的准确性，而是优先保障分类性能。模型中设计了专门的多尺度特征学习模块，用于从含噪声的插补数据中提取有用信息。我们在UCR归档库的68个单变量时间序列数据集、一个具有不同缺失数据比例的多变量时间序列数据集，以及4个含缺失信息的真实世界数据集上对所提模型进行了评估。结果表明，该模型在不完整时间序列分类任务上优于当前最优方法，尤其在缺失数据比例较高的场景中表现更突出。

英文摘要：

Time series classification with missing data is a prevalent issue in time series analysis, as temporal data often contain missing values in practical applications. The traditional two-stage approach, which handles imputation and classification separately, can result in sub-optimal performance as label information is not utilized in the imputation process. On the other hand, a one-stage approach can learn features under missing information, but feature representation is limited as imputed errors are propagated in the classification process. To overcome these challenges, this study proposes an end-to-end neural network that unifies data imputation and representation learning within a single framework, allowing the imputation process to take advantage of label information. Differing from previous methods, our approach places less emphasis on the accuracy of imputation data and instead prioritizes classification performance. A specifically designed multi-scale feature learning module is implemented to extract useful information from the noise-imputation data. The proposed model is evaluated on 68 univariate time series datasets from the UCR archive, as well as a multivariate time series dataset with various missing data ratios and 4 real-world datasets with missing information. The results indicate that the proposed model outperforms state-of-the-art approaches for incomplete time series classification, particularly in scenarios with high levels of missing data.

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