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2013

Temporal Autoencoding Improves Generative Models of Time Series

Häusler, Chris, Susemihl, Alex, Nawrot, Martin P et al.

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Restricted Boltzmann Machines (RBMs) are generative models which can learn useful representations from samples of a dataset in an unsupervised fashion.

They have been widely employed as an unsupervised pre-training method in machine learning.
RBMs have been modified to model time series in two main ways: The Temporal RBM stacks a number of RBMs laterally and introduces temporal dependencies between the hidden layer units; The Conditional RBM, on the other hand, considers past samples of the dataset as a conditional bias and learns a representation which takes these into account.
Here we propose a new training method for both the TRBM and the CRBM, which enforces the dynamic structure of temporal datasets.

Temporal Autoencoding Improves Generative Models of Time Series · Around

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