Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: From physical linear systems to discrete-time series. A guide for
  analysis of the sampled experimental data   Modelling physical data with linear discrete time series, namely Fractionally
Integrated Autoregressive Moving Average (ARFIMA), is a technique which
achieved attention in recent years. However, these models are used mainly as a
statistical tool only, with weak emphasis on physical background of the model.
The main reason for this lack of attention is that ARFIMA model describes
discrete-time measurements, whereas physical models are formulated using
continuous-time parameter. In order to remove this discrepancy we show that
time series of this type can be regarded as sampled trajectories of the
coordinates governed by system of linear stochastic differential equations with
constant coefficients. The observed correspondence provides formulas linking
ARFIMA parameters and the coefficients of the underlying physical stochastic
system, thus providing a bridge between continuous-time linear dynamical
systems and ARFIMA models.

36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
2
The categories of this paper are: math
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph