Pospisil, L. and Gagliardini, P. and Sawyer, W. and Horenko, I. (2018) On a scalable nonparametric denoising of time series signals. Communications in Applied Mathematics and Computational Science, 13 (1). pp. 107138.

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Official URL: http://DOI: 10.2140/camcos.2018.13.107
Abstract
Denoising and filtering of time series signals is a problem emerging in many areas of computational science. Here we demonstrate how the nonparametric computational methodology of the finite element method of time series analysis with H1 regularization can be extended for denoising of very long and noisy time series signals. The main computational bottleneck is the inner quadratic programming problem. Analyzing the solvability and utilizing the problem structure, we suggest an adapted version of the spectral projected gradient method (SPGQP) to resolve the problem. This approach increases the granularity of parallelization, making the proposed methodology highly suitable for graphics processing unit (GPU) computing. We demonstrate the scalability of our opensource implementation based on PETSc for the Piz Daint supercomputer of the Swiss Supercomputing Centre (CSCS) by solving largescale data denoising problems and comparing their computational scaling and performance to the performance of the standard denoising methods.
Item Type:  Article 

Additional Information:  SFB 1114 Preprint: 06/2017 
Subjects:  Mathematical and Computer Sciences > Mathematics > Applied Mathematics 
Divisions:  Department of Mathematics and Computer Science > Institute of Mathematics 
ID Code:  2127 
Deposited By:  Silvia Hoemke 
Deposited On:  21 Nov 2017 09:22 
Last Modified:  27 Apr 2021 13:01 
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