L_1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations
نویسندگان
چکیده
منابع مشابه
Time Series Models With Asymmetric Laplace Innovations
We propose autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedastic (GARCH) models driven by Asymmetric Laplace (AL) noise. The AL distribution plays, in the geometric-stable class, the analogous role played by the normal in the alpha-stable class, and has shown promise in the modeling of certain types of financial and engineering data. In the case of an A...
متن کاملSparse Regularization for High Dimensional Additive Models
We study the behavior of the l1 type of regularization for high dimensional additive models. Our results suggest remarkable similarities and differences between linear regression and additive models in high dimensional settings. In particular, our analysis indicates that, unlike in linear regression, l1 regularization does not yield optimal estimation for additive models of high dimensionality....
متن کاملModelling High Dimensional Time Series by Generalized Factor Models
We discuss and analyze generalized linear dynamic factor models. These models have been developed recently and they are used to model high dimensional time series in order to overcome the “curse of dimensionality”. The basic idea in factor models is to seperate “comovement” between the variables (caused by a relatively small number of factors) from individual (idiosyncratic) variation. Here fac...
متن کاملTwo Distributed-State Models For Generating High-Dimensional Time Series
In this paper we develop a class of nonlinear generative models for high-dimensional time series. We first propose a model based on the restricted Boltzmann machine (RBM) that uses an undirected model with binary latent variables and real-valued “visible” variables. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-st...
متن کاملComposable, distributed-state models for high-dimensional time series
Composable, distributed-state models for high-dimensional time series Graham William Taylor Doctor of Philosophy Graduate Department of Computer Science University of Toronto, 2009 In this thesis we develop a class of nonlinear generative models for highdimensional time series. The first key property of these models is their distributed, or “componential” latent state, which is characterized by...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2015
ISSN: 1556-5068
DOI: 10.2139/ssrn.2626507