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.
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Accepted/In Press date: October 2008
Keywords:
system identification, sparse approximation, l1 regularization
Organisations:
Southampton Wireless Group
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Local EPrints ID: 266779
URI: http://eprints.soton.ac.uk/id/eprint/266779
PURE UUID: 7fe3ac88-7f08-4fdc-b07c-2651f8b7322d
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Date deposited: 13 Oct 2008 08:35
Last modified: 14 Mar 2024 08:35
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Author:
Ivan Markovsky
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