Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Improved Online Penalty Parameter Selection Procedure for
  $\ell_1$-Penalized Autoregressive with Exogenous Variables   Many recent developments in the high-dimensional statistical time series
literature have centered around time-dependent applications that can be adapted
to regularized least squares. Of particular interest is the lasso, which both
serves to regularize and provide feature selection. The lasso requires the
specification of a penalty parameter that determines the degree of sparsity to
impose. The most popular penalty parameter selection approaches that respect
time dependence are very computationally intensive and are not appropriate for
modeling certain classes of time series. We propose enhancing a canonical time
series model, the autoregressive model with exogenous variables, with a novel
online penalty parameter selection procedure that takes advantage of the
sequential nature of time series data to improve both computational performance
and forecast accuracy relative to existing methods in both a simulation and
empirical application involving macroeconomic indicators.

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The categories of this paper are: cond-mat
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The categories of this paper are: math
0
The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: cs