Learning Periods from Incomplete Multivariate Time Series
Learning Periods from Incomplete Multivariate Time Series
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DOI：
10.1109/icdm50108.2020.00183
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Lin Zhang;A. Gorovits;Wenyu Zhang;Petko Bogdanov
中科院分区：
文献类型：
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作者：
Lin Zhang;A. Gorovits;Wenyu Zhang;Petko Bogdanov
时间序列中季节性的建模和检测对于准确的分析、预测和异常检测是必不可少的。不同规模的季节性影响的例子比比皆是：节日期间消费品销售的增长每年都会出现，同样，家庭用电量也有每日、每周和每年的周期。然而，现实世界时间序列中的周期可能会被数据采集中产生的噪声和缺失值所混淆。如何从不完整的多元时间序列中学习自然周期性？我们提出了一个强大的框架多变量周期检测，称为LAPIS。它通过Ramanujan周期字典将不完整和有噪声的数据编码为稀疏摘要。LAPIS可以准确地检测同一时间序列中多个时段的混合，即使70%的观测数据缺失。我们的框架的一个关键创新是，它利用跨单个时间序列的共享周期，即使它们不相关或不同步。除了检测周期外，LAPIS还可以改进下游应用程序，如预测、缺失值填补和聚类。与此同时，我们的方法可以扩展到在长达50万个时间点的数据集上在几秒钟内执行的大型真实数据。
Modeling and detection of seasonality in time series is essential for accurate analysis, prediction and anomaly detection. Examples of seasonal effects at different scales abound: the increase in consumer product sales during the holiday season recurs yearly, and similarly household electricity usage has daily, weekly and yearly cycles. The period in real-world time series, however, may be obfuscated by noise and missing values arising in data acquisition. How can one learn the natural periodicity from incomplete multivariate time series? We propose a robust framework for multivariate period detection, called LAPIS. It encodes incomplete and noisy data as a sparse summary via a Ramanujan periodic dictionary. LAPIS can accurately detect a mixture of multiple periods in the same time series even when 70% of the observations are missing. A key innovation of our framework is that it exploits shared periods across individual time series even when they are not correlated or in-phase. Beyond detecting periods, LAPIS enables improvements in downstream applications such as forecasting, missing value imputation and clustering. At the same time our approach scales to large real-world data executing within seconds on datasets of length up to half a million time points.