Time series prediction with FNN-LSTM
This article is originally published at https://blogs.rstudio.com/tensorflow/In a recent post, we showed how an LSTM autoencoder, regularized by false nearest neighbors (FNN) loss, can be used to reconstruct the attractor of a nonlinear, chaotic dynamical system. Here, we explore how that same technique assists in prediction. Matched up with a comparable, capacity-wise, "vanilla LSTM", FNN-LSTM improves performance on a set of very different, real-world datasets, especially for the initial steps in a multi-step forecast.
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This article is originally published at https://blogs.rstudio.com/tensorflow/
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