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首页> 外文期刊>Engineering Applications of Artificial Intelligence >VASP: An autoencoder-based approach for multivariate anomaly detection and robust time series prediction with application in motorsport
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VASP: An autoencoder-based approach for multivariate anomaly detection and robust time series prediction with application in motorsport

机译：VASP：基于AutoEncoder的方法，用于多变量异常检测和强大的时间序列预测，在Motorsport中的应用

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摘要

The aim is to provide a framework for robust time series prediction in the presence of anomalies. The framework is developed based on a data set from motorsport but is not limited to this specific area. In motorsport, the usage of sensors during races is generally restricted. Estimating the outputs of these missing sensors therefore provides an advantage over the competition. Deep learning approaches such as long short-term memory (LSTM) neural networks have proven to be useful for that task, however, their accuracy decreases significantly if anomalies occur in the input signals. To overcome this problem, we propose the variational autoencoder based selective prediction (VASP) framework which combines the tasks of anomaly detection and time series prediction. VASP consists of a variational autoencoder (VAE), an anomaly detector and LSTM predictors. Depending on the anomaly detector, a subset of the inputs may be replaced by the VAE, allowing a more robust prediction. To the best of our knowledge the approach of using a VAE to only selectively replace anomalous input data before prediction has not yet been published. Our contributions are clear implementation guidelines and a comparison to other VAE-based methods and a LSTM approach as baseline. We simulate anomalies with three approaches and show that VASP outperforms other methods by having no trade-off between accuracy and robustness. VASP is as accurate as the baseline for regular data, but for anomalous inputs the error is reduced by 13% to 33% on average and up to 70% in special cases.
机译：目的是在异常存在下提供鲁棒时间序列预测的框架。该框架是根据来自Motorsport的数据集开发的，但不限于此特定区域。在Motorsport中，赛跑期间的传感器的使用通常受到限制。因此，估计这些缺失传感器的输出提供了对竞争的优势。已经证明了长期内存（LSTM）神经网络（LSTM）神经网络等深入学习方法对于该任务有用，然而，如果在输入信号中发生异常，它们的准确性会显着降低。为了克服这个问题，我们提出了基于变化的自动级别的选择性预测（VASP）框架，其组合了异常检测和时间序列预测的任务。 VASP由变形AutoEncoder（VAE），异常检测器和LSTM预测器组成。根据异常检测器，输入的子集可以由VAE代替，允许更稳健的预测。据我们所知，使用VAE的方法仅在预测之前选择性地替换异常输入数据。我们的贡献是明确的实施指南和与其他基于VAE的方法和LSTM方法相比作为基线。我们模拟了三种方法的异常，并表明VASP通过在准确性和鲁棒性之间没有权衡来实现其他方法。 VASP与常规数据的基线一样准确，但对于异常输入，误差平均减少13％至33％，特殊情况下高达70％。

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