This post did not end up quite the way I'd imagined. A quick follow-up on the recent Time series prediction with FNN-LSTM, it was supposed to demonstrate how noisy time series (so common in practice) could profit from a change in architecture: Instead of FNN-LSTM, an LSTM autoencoder regularized by false nearest neighbors (FNN) loss, use FNN-VAE, a variational autoencoder constrained by the same. However, FNN-VAE did not seem to handle noise better than FNN-LSTM. On the other hand – this is not a scientific study, with hypothesis and experimental setup all preregistered; all that really matters is if there's something useful to report. And it looks like there is.