Universal Event Detection in Time Series.
CoRR(2023)
摘要
In our previously published work, we introduced a supervised deep learning
method for event detection in multivariate time series data, employing
regression instead of binary classification. This simplification avoids the
need for point-wise labels throughout the entire dataset, relying solely on
ground truth events defined as time points or intervals. In this paper, we
establish mathematically that our method is universal, and capable of detecting
any type of event with arbitrary precision under mild continuity assumptions on
the time series. These events may encompass change points, frauds, anomalies,
physical occurrences, and more. We substantiate our theoretical results using
the universal approximation theorem for feed-forward neural networks (FFN).
Additionally, we provide empirical validations that confirm our claims,
demonstrating that our method, with a limited number of parameters, outperforms
other deep learning approaches, particularly for rare events and imbalanced
datasets from different domains.
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