Optimal Estimate of Monotonic Trend with Sparse Jumps

Dimitry Gorinevsky
2007 American Control Conference (ACC)  
This paper discusses a problem for recovering an underlying trend from noisy data. The key assumption is that the trend is monotonic, e.g., reflects accumulation of irreversible system deterioration. The trend is obtained as a maximum a posteriori probability estimate. The overall problem setup is related to α-β filter and Hodrick-Prescott filter. The main difference is that instead of a Gaussian process noise, a onesided exponentially distributed noise is assumed. The batch estimate is a
more » ... on to a Quadratic Programming problem. The approach works exceptionally well for piece-wise linear trends that have a small number of jumps in the trended variable or its increase rate. Theoretical analysis justifies the sparsity properties for the jumps in the solution.
doi:10.1109/acc.2007.4282395 dblp:conf/acc/Gorinevsky07 fatcat:nq22mxsqtvc2xn74t4ocw2saxu