Period Estimation For Incomplete Time Series
Period Estimation For Incomplete Time Series
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DOI：
10.1109/dsaa49011.2020.00016
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Lin Zhang;Petko Bogdanov
中科院分区：
文献类型：
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作者：
Lin Zhang;Petko Bogdanov
自然和人造系统经常表现出周期性行为。例如气候系统、野生动物迁徙、电网电力消耗等。然而，此类系统的行为并不完全周期性。由于数据收集和传输的限制，或者由于传感器故障和中断，我们从它们那里收集的时间序列通常充满噪音且不完整。此外，通常存在多个周期，例如，气温和压力随季节每天和每年波动。因此，根据原始时间序列进行准确而鲁棒的周期估计是流量预测和异常检测等下游应用中经常采用的一项基本任务。在本文中，我们研究了具有多个周期和缺失值的噪声时间序列中的周期估计问题。我们提出了一种基于拉马努金周期词典和向量补全模型的方法来估计缺失值。为了解释拉马努金周期词典中的块结构，我们引入了图拉普拉斯群套索正则化，它可以在存在缺失观测值的情况下实现稳健且高效的周期学习。在我们对不同领域的数据集进行的广泛实验中，我们提出的方法在周期估计的准确性方面优于最先进的基线。
Natural and human-engineered systems often exhibit periodic behavior. Examples include the climate system, migration of animals in the wild, consumption of electricity in the power grid and others. The behavior of such systems, however, is not perfectly periodic. The time series we collect from them are often noisy and incomplete due to limitations of data collection and transmission, or due to sensor malfunction and outages. In addition, there are often multiple periods, for example, air temperature and pressure oscillates daily and yearly with the seasons. Hence, accurate and robust period estimation from raw time series is a fundamental task often employed in downstream applications such as traffic prediction and anomaly detection.In this paper, we study the period estimation problem in noisy time series with multiple periods and missing values. We propose a method based on a Ramanujan periodic dictionary and a vector completion model to estimate missing values. To account for the block structure in the Ramanujan periodic dictionary, we introduce a graph Laplacian group lasso regularization which enables robust and efficient period learning in the presence of missing observations. In our extensive experiments on datasets from diverse domains, our proposed methodology outperforms state-of-art baselines in terms of accuracy of period estimation.