Mixed Membership Models for Time Series release_2ghqdzyksze45kv2uakr7fxnnm

by Emily B. Fox, Michael I. Jordan

Released as a article .

2013  

Abstract

In this article we discuss some of the consequences of the mixed membership perspective on time series analysis. In its most abstract form, a mixed membership model aims to associate an individual entity with some set of attributes based on a collection of observed data. Although much of the literature on mixed membership models considers the setting in which exchangeable collections of data are associated with each member of a set of entities, it is equally natural to consider problems in which an entire time series is viewed as an entity and the goal is to characterize the time series in terms of a set of underlying dynamic attributes or "dynamic regimes". Indeed, this perspective is already present in the classical hidden Markov model, where the dynamic regimes are referred to as "states", and the collection of states realized in a sample path of the underlying process can be viewed as a mixed membership characterization of the observed time series. Our goal here is to review some of the richer modeling possibilities for time series that are provided by recent developments in the mixed membership framework.
In text/plain format

Archived Files and Locations

application/pdf  1.4 MB
file_vnezxjqyqrcfvj66i2yfhaovqi
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2013-09-13
Version   v1
Language   en ?
arXiv  1309.3533v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: aee974db-5f8b-4395-b2ae-7391cca529ac
API URL: JSON