Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Benign Overfitting in Time Series Linear Model with
  Over-Parameterization   The success of large-scale models in recent years has increased the
importance of statistical models with numerous parameters. Several studies have
analyzed over-parameterized linear models with high-dimensional data that may
not be sparse; however, existing results depend on the independent setting of
samples. In this study, we analyze a linear regression model with dependent
time series data under over-parameterization settings. We consider an estimator
via interpolation and developed a theory for excess risk of the estimator under
multiple dependence types. This theory can treat infinite-dimensional data
without sparsity and handle long-memory processes in a unified manner.
Moreover, we bound the risk in our theory via the integrated covariance and
nondegeneracy of autocorrelation matrices. The results show that the
convergence rate of risks with short-memory processes is identical to that of
cases with independent data, while long-memory processes slow the convergence
rate. We also present several examples of specific dependent processes that can
be applied to our setting.

17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th