4 research outputs found
Generative Learning of Heterogeneous Tail Dependence
We propose a multivariate generative model to capture the complex dependence
structure often encountered in business and financial data. Our model features
heterogeneous and asymmetric tail dependence between all pairs of individual
dimensions while also allowing heterogeneity and asymmetry in the tails of the
marginals. A significant merit of our model structure is that it is not prone
to error propagation in the parameter estimation process, hence very scalable,
as the dimensions of datasets grow large. However, the likelihood methods are
infeasible for parameter estimation in our case due to the lack of a
closed-form density function. Instead, we devise a novel moment learning
algorithm to learn the parameters. To demonstrate the effectiveness of the
model and its estimator, we test them on simulated as well as real-world
datasets. Results show that this framework gives better finite-sample
performance compared to the copula-based benchmarks as well as recent similar
models
Memory-Gated Recurrent Networks
The essence of multivariate sequential learning is all about how to extract
dependencies in data. These data sets, such as hourly medical records in
intensive care units and multi-frequency phonetic time series, often time
exhibit not only strong serial dependencies in the individual components (the
"marginal" memory) but also non-negligible memories in the cross-sectional
dependencies (the "joint" memory). Because of the multivariate complexity in
the evolution of the joint distribution that underlies the data generating
process, we take a data-driven approach and construct a novel recurrent network
architecture, termed Memory-Gated Recurrent Networks (mGRN), with gates
explicitly regulating two distinct types of memories: the marginal memory and
the joint memory. Through a combination of comprehensive simulation studies and
empirical experiments on a range of public datasets, we show that our proposed
mGRN architecture consistently outperforms state-of-the-art architectures
targeting multivariate time series.Comment: This paper was accepted and will be published in the Thirty-Fifth
AAAI Conference on Artificial Intelligence (AAAI-21