Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prediction in locally stationary time series   We develop an estimator for the high-dimensional covariance matrix of a
locally stationary process with a smoothly varying trend and use this statistic
to derive consistent predictors in non-stationary time series. In contrast to
the currently available methods for this problem the predictor developed here
does not rely on fitting an autoregressive model and does not require a
vanishing trend. The finite sample properties of the new methodology are
illustrated by means of a simulation study and a financial indices study.

23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
1
The categories of this paper are: cs