dynamic filtering

Wednesday, April 25, 2018 - 3:30pm - 4:00pm
Christopher Rozell (Georgia Institute of Technology)
Tracking time-varying signals is an important part of forecasting in complex time-series data. Recently, signal processing techniques have been developed to improve tracking performance when the signal of interest is known a-priori to be sparse. In this talk we will review a collection of related algorithms we have developed for dynamic filtering of time-varying sparse signals. The foundations of this work are based on two algorithms that leverage efficient L1 optimization methods.
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