Dimension Reduction for time series with Variational AutoEncoders
This work addresses dimensionality reduction for noisy real-world time series data like ECG, but it is incremental as it compares existing methods without introducing new techniques.
The paper compared wavelet decomposition and convolutional variational autoencoders for dimensionality reduction on ECG time series data, finding that VAEs effectively reduce dimensions and are robust to noisy training and inference conditions.
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.