The University of Southampton
University of Southampton Institutional Repository

Results on the PASCAL challenge "Simple causal effects in time series"

Results on the PASCAL challenge "Simple causal effects in time series"
Results on the PASCAL challenge "Simple causal effects in time series"
A solution to the PASCAL challenge "Simple causal effects in time series" (www.causality.inf.ethz.ch) is presented. The data is modeled as a sum of a constant-plus-sin term and a term that is a linear function of a small number of inputs. The problem of identifying such a model from the data is nonconvex in the frequency and phase parameters of the sin and is combinatorial in the number of inputs. The proposed method is suboptimal and exploits several heuristics. First, the problem is split into two phases: 1) identification of the autonomous part and 2) identification of the input dependent part. Second, local optimization method is used to solve the problem in the first phase. Third, l1 regularization is used in order to find a sparse solution in the second phase.
system identification, sparse approximation, l1 regularization
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c

Markovsky, Ivan (2008) Results on the PASCAL challenge "Simple causal effects in time series" (In Press)

Record type: Monograph (Project Report)

Abstract

A solution to the PASCAL challenge "Simple causal effects in time series" (www.causality.inf.ethz.ch) is presented. The data is modeled as a sum of a constant-plus-sin term and a term that is a linear function of a small number of inputs. The problem of identifying such a model from the data is nonconvex in the frequency and phase parameters of the sin and is combinatorial in the number of inputs. The proposed method is suboptimal and exploits several heuristics. First, the problem is split into two phases: 1) identification of the autonomous part and 2) identification of the input dependent part. Second, local optimization method is used to solve the problem in the first phase. Third, l1 regularization is used in order to find a sparse solution in the second phase.

Text
challenge.pdf - Other
Download (163kB)
Archive
challenge.tar - Other
Download (10kB)

More information

Accepted/In Press date: October 2008
Keywords: system identification, sparse approximation, l1 regularization
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 266779
URI: http://eprints.soton.ac.uk/id/eprint/266779
PURE UUID: 7fe3ac88-7f08-4fdc-b07c-2651f8b7322d

Catalogue record

Date deposited: 13 Oct 2008 08:35
Last modified: 14 Mar 2024 08:35

Export record

Contributors

Author: Ivan Markovsky

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×