Dynamic adaptive partitioning for nonlinear time series

Dynamic Adaptive Partitioning for Nonlinear Time Series Dynamic Adaptive Partitioning for Nonlinear Time Series

We propose a dynamic adaptive partitioning scheme for nonparametric analysis of stationary nonlinear time series with values in IR d (d 1). We use information from past values to construct adaptive partitioning in a dynamic fashion which is then diierent from the more common static schemes in the regression setup. The idea of dynamic partitioning is novel. We make it constructive by proposing a...

Dynamic Adaptive Partitioning for Nonlinear Time Series

Dynamic Adaptive Partitioning for Nonlinear Time Series

We propose a dynamic adaptive partitioning scheme for nonparametric analysis of stationary nonlinear time series. It yields estimates of the whole probability distribution of the underlying process. We use information from past values to construct adaptive partitioning in a dynamic fashion which is then diierent from the more common static schemes in the regression setup. The idea of dynamic pa...

A Neural Architecture for Nonlinear Adaptive Filtering of Time Series

Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction

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...