Results on the PASCAL challenge "Simple causal effects in time series" s.n.
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
||Southampton Wireless Group
|October 2008||Accepted/In Press|
||13 Oct 2008 08:35
||17 Apr 2017 18:58
|Further Information:||Google Scholar|
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