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Time Series Forecasting via Learning Convolutionally Low-Rank Models. (arXiv:2104.11510v5 [cs.LG] UPDATED)
Jan. 21, 2022, 2:11 a.m. | Guangcan Liu
cs.LG updates on arXiv.org arxiv.org
Recently, Liu and Zhang studied the rather challenging problem of time series
forecasting from the perspective of compressed sensing. They proposed a
no-learning method, named Convolution Nuclear Norm Minimization (CNNM), and
proved that CNNM can exactly recover the future part of a series from its
observed part, provided that the series is convolutionally low-rank. While
impressive, the convolutional low-rankness condition may not be satisfied
whenever the series is far from being seasonal, and is in fact brittle to the
presence …
arxiv forecasting learning time time series time series forecasting
More from arxiv.org / cs.LG updates on arXiv.org
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