Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prediction of weakly locally stationary processes by auto-regression   In this contribution we introduce weakly locally stationary time series
through the local approximation of the non-stationary covariance structure by a
stationary one. This allows us to define autoregression coefficients in a
non-stationary context, which, in the particular case of a locally stationary
Time Varying Autoregressive (TVAR) process, coincide with the generating
coefficients. We provide and study an estimator of the time varying
autoregression coefficients in a general setting. The proposed estimator of
these coefficients enjoys an optimal minimax convergence rate under limited
smoothness conditions. In a second step, using a bias reduction technique, we
derive a minimax-rate estimator for arbitrarily smooth time-evolving
coefficients, which outperforms the previous one for large data sets. In turn,
for TVAR processes, the predictor derived from the estimator exhibits an
optimal minimax prediction rate.

1
The categories of this paper are: cs
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn