A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data
A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data
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DOI：
10.1080/00949655.2017.1299151
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发表时间：
2017-03
影响因子：
1.2
通讯作者：
L. Ren;Y. Wei;Jin Cui;Yi Du
中科院分区：
文献类型：
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作者：
L. Ren;Y. Wei;Jin Cui;Yi Du
时间序列数据分析，如时间模式识别和趋势预测，在时间数据统计和分析中起着越来越重要的作用。然而，对于大型多元时间序列的模式提取和趋势预测的效率仍然存在挑战。提出了一种基于动态滑动时间窗的多变量时间序列多阶段聚类方法。该方法首先对分割后的多变量时间序列在每个时间窗内进行聚类，生成第一阶段的聚类中心，然后利用第一阶段的聚类中心生成包含所有时间窗的第二阶段聚类结果。该方法通过在多个滑动时间窗口上进行多阶段聚类，简化了大规模多变量时间序列挖掘问题，提高了挖掘效率。在聚类结果的基础上，提出了一种关联规则挖掘方法，用于发现时间序列中的频繁模式，生成自关联规则。然后，本文提出了一个概率预测模型，利用提取的规则进行短期预测。最后，通过实验验证了该方法的有效性.
ABSTRACT Time series data analysis, such as temporal pattern recognition and trend forecasting, plays an increasingly significant part in temporal data statistics and analytics. Yet challenges still exist in the efficiency of pattern extracting and trend prediction for large multivariate time series. The paper proposes a multi-stage clustering approach towards multivariate time series by using dynamic sliding time windows. The segmented multivariate time series are clustered separately in each time window to product first-stage clustering centres, and which are used to generate second-stage clustering results involving all time windows. The method can simplify large scale multivariate time series mining problems through multi-stage clustering on multiple sliding time windows thus achieve improved efficiency. Based on the clustering outcomes, a correlation rules mining method is given to discover frequent patterns in the time series and generate self-correlation rules. Then, the paper presents a probabilistic forecasting model that leverages the extracted rules to make short-term predictions. Finally, experiments are presented to show the efficiency and effectiveness of the proposed approach.