Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review
Jonathan H. Gillard, Konstantin Usevich
Abstract
In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting.We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper closes with some expository examples. Keywords
Topics & Concepts
Rank (graph theory)Series (stratigraphy)Key (lock)Computer scienceHankel matrixLow-rank approximationTime seriesMathematicsMathematical optimizationEconometricsCalculus (dental)Applied mathematicsMachine learningCombinatoricsComputer securityMathematical analysisBiologyPaleontologyMedicineDentistryStatistical and numerical algorithmsSparse and Compressive Sensing TechniquesImage and Signal Denoising Methods