论文标题
缩小时间序列的差异自动编码器
Dimension Reduction for time series with Variational AutoEncoders
论文作者
论文摘要
在这项工作中，我们探索了单变量和多变量时间序列数据的降低降低技术。我们特别进行了小波分解与卷积变分自动编码器之间的比较，以减小尺寸。我们表明，变异自动编码器是降低ECG等高维数据的维度的好选择。我们在现实世界（公开可用）的ECG数据集上进行了这些比较，该数据集具有很大的可变性，并将重建误差用作度量标准。然后，我们使用嘈杂的数据来探索这些模型的鲁棒性，无论是用于培训还是推理。这些测试旨在反映现实世界中时间序列数据中存在的问题，而VAE对这两种测试都有鲁棒性。
In this work, we explore dimensionality reduction techniques for univariate and multivariate time series data. We especially conduct a comparison between wavelet decomposition and convolutional variational autoencoders for dimension reduction. We show that variational autoencoders are a good option for reducing the dimension of high dimensional data like ECG. We make these comparisons on a real world, publicly available, ECG dataset that has lots of variability and use the reconstruction error as the metric. We then explore the robustness of these models with noisy data whether for training or inference. These tests are intended to reflect the problems that exist in real-world time series data and the VAE was robust to both tests.
