Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recovery of Future Data via Convolution Nuclear Norm Minimization   This paper studies the problem of time series forecasting (TSF) from the
perspective of compressed sensing. First of all, we convert TSF into a more
inclusive problem called tensor completion with arbitrary sampling (TCAS),
which is to restore a tensor from a subset of its entries sampled in an
arbitrary manner. While it is known that, in the framework of Tucker
low-rankness, it is theoretically impossible to identify the target tensor
based on some arbitrarily selected entries, in this work we shall show that
TCAS is indeed tackleable in the light of a new concept called convolutional
low-rankness, which is a generalization of the well-known Fourier sparsity.
Then we introduce a convex program termed Convolution Nuclear Norm Minimization
(CNNM), and we prove that CNNM succeeds in solving TCAS as long as a sampling
condition--which depends on the convolution rank of the target tensor--is
obeyed. This theory provides a meaningful answer to the fundamental question of
what is the minimum sampling size needed for making a given number of
forecasts. Experiments on univariate time series, images and videos show
encouraging results.

18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga