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Efficient pattern matching for uncertain time series data with optimal sampling and dimensionality reduction

机译：具有最佳采样的不确定时间序列数据的高效模式匹配和维数减少

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Time series data mining becomes an active research area due to the rapid proliferation of temporal-dependent applications. Dimensionality reduction and uncertainty handling play a pivotal role in extracting the time series pattern. Most of the dimensionality reduction schemes are designed based on the assumption that every class of samples follows the Gaussian distribution. Lack of this property in real time data distribution does not allow dimensionality reduction techniques to characterize the different classes well and measure the data uncertainty accurately. In addition to, applying an uncertainty measurement evenly on inconsistent time series data samples may underestimate the source of uncertainty among various sub-samples. This paper presents the Handling UNcertainty and missing value prediction in Time series (HUNT). The proposed approach employs Adaptive Reservoir Filling for sampling the time series and Discrepant Sample dependent Chebyshev inequality for handling the uncertainty. The HUNT implements the adaptive reservoir filling using discrepancy estimation over a statistical population and decides the reservoir size according to the variations in the data stream. The state of the statistical population ensures the uncertainty handling over discrepant samples. The proposed approach precisely replaces the missing values with the support of the Mean-Mode imputation method. To effectively select the key features, it applies both the indirect and direct performance measures on the statistical samples. Finally, the proposed model generates the fine-tuned statistical samples through segmentation to facilitate the time series pattern matching. The experimental results demonstrate that the HUNT approach significantly outperforms the existing time series pattern matching approaches such as KSample approach by 18% higher recall and UG-Miner approach by 20% minimum Mean Absolute Error (MAE) while testing on the Weather forecasting dataset. (C) 2020 Elsevier B.V. All rights reserved.
机译：由于时间依赖性应用的快速增殖，时间序列数据挖掘成为一个活跃的研究区。维数减少和不确定性处理在提取时间序列模式方面发挥着关键作用。大多数维度减少方案是基于假设每类样本遵循高斯分布的假设。实时数据分布缺乏此属性不允许效果良好地表征不同类别的维度降低技术，并准确地测量数据不确定性。除了在不一致的时间序列数据样本上均匀地施加不确定性测量可能低估了各种子样本之间的不确定性源。本文介绍了处理时间序列（亨特）的处理不确定性和缺失值预测。该方法采用自适应储层填充，用于取样时间序列和差异样本依赖的Chebyshev不等式以处理不确定性。捕冲器利用统计群体的差异估计实现自适应储存器填充，并根据数据流的变化来确定储存器大小。统计群体的状态确保了对差异样本的不确定性处理。通过支持均值载体方法，建议的方法精确地替换了缺失的值。为了有效地选择关键特征，它可以在统计样本上采用间接和直接性能措施。最后，所提出的模型通过分割产生微调统计样本，以便于时间序列模式匹配。实验结果表明，寻线方法显着优于现有的时间序列模式匹配方法，如ksample方法，在天气预报数据集上测试时，在20％的最小平均绝对误差（MAE）时，ksample方法如ksample方法。 （c）2020 Elsevier B.v.保留所有权利。

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