1,951 research outputs found
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems
The demand of probabilistic time series forecasting has been recently raised
in various dynamic system scenarios, for example, system identification and
prognostic and health management of machines. To this end, we combine the
advances in both deep generative models and state space model (SSM) to come up
with a novel, data-driven deep probabilistic sequence model. Specially, we
follow the popular encoder-decoder generative structure to build the recurrent
neural networks (RNN) assisted variational sequence model on an augmented
recurrent input space, which could induce rich stochastic sequence dependency.
Besides, in order to alleviate the issue of inconsistency between training and
predicting as well as improving the mining of dynamic patterns, we (i) propose
using a hybrid output as input at next time step, which brings training and
predicting into alignment; and (ii) further devise a generalized
auto-regressive strategy that encodes all the historical dependencies at
current time step. Thereafter, we first investigate the methodological
characteristics of the proposed deep probabilistic sequence model on toy cases,
and then comprehensively demonstrate the superiority of our model against
existing deep probabilistic SSM models through extensive numerical experiments
on eight system identification benchmarks from various dynamic systems.
Finally, we apply our sequence model to a real-world centrifugal compressor
sensor data forecasting problem, and again verify its outstanding performance
by quantifying the time series predictive distribution.Comment: 25 pages, 7 figures, 4 tables, preprint under revie
Identifying Nonlinear 1-Step Causal Influences in Presence of Latent Variables
We propose an approach for learning the causal structure in stochastic
dynamical systems with a -step functional dependency in the presence of
latent variables. We propose an information-theoretic approach that allows us
to recover the causal relations among the observed variables as long as the
latent variables evolve without exogenous noise. We further propose an
efficient learning method based on linear regression for the special sub-case
when the dynamics are restricted to be linear. We validate the performance of
our approach via numerical simulations
Weighted-Lasso for Structured Network Inference from Time Course Data
We present a weighted-Lasso method to infer the parameters of a first-order
vector auto-regressive model that describes time course expression data
generated by directed gene-to-gene regulation networks. These networks are
assumed to own a prior internal structure of connectivity which drives the
inference method. This prior structure can be either derived from prior
biological knowledge or inferred by the method itself. We illustrate the
performance of this structure-based penalization both on synthetic data and on
two canonical regulatory networks, first yeast cell cycle regulation network by
analyzing Spellman et al's dataset and second E. coli S.O.S. DNA repair network
by analysing U. Alon's lab data
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