Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction

  title={Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction},
  author={Sakyasingha Dasgupta and Takayuki Osogami},
The dynamic Boltzmann machine (DyBM) has been proposed as a stochastic generative model of multi-dimensional time series, with an exact, learning rule that maximizes the log-likelihood of a given time series. The DyBM, however, is defined only for binary valued data, without any nonlinear hidden units. Here, in our first contribution, we extend the DyBM to deal with real valued data. We present a formulation called Gaussian DyBM, that can be seen as an extension of a vector autoregressive (VAR… CONTINUE READING
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