FNN-VAE for noisy time series forecasting
This article is originally published at https://blogs.rstudio.com/tensorflow/In the last part of this mini-series on forecasting with false nearest neighbors (FNN) loss, we replace the LSTM autoencoder from the previous post by a convolutional VAE, resulting in equivalent prediction performance but significantly lower training time. In addition, we find that FNN regularization is of great help when an underlying deterministic process is obscured by substantial noise.
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This article is originally published at https://blogs.rstudio.com/tensorflow/
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