Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Time-varying Parameter Based Seasonally-adjusted Bayesian State-space
  Model for Forecasting   In this paper, we develop a time-varying parameter based seasonally-adjusted
Bayesian state-space model for non-stationary time series datasets where both
the trend and seasonal components are present and it is the general scenario
for most of the real datasets in various scientific disciplines. In spite of
removing such terms using some do-and-check procedure to make the data
stationary, our model directly fits a dataset and forecasts a number of future
observations. For a specific prior construction we have considered, every
parameter update is one-dimensional so that we don't need to invert any matrix
and also we overcome the difficulty of Metropolis-Hastings steps simply by
Gibbs sampling which is another advantage of this model. It can handle missing
data as well which occurs very often in time series contexts. We implement it
on the sufficiently large (24 years of monthly average temperature series, i.e.
the number of observations =288) for 57 meteorological stations across India
and show that for most of the cases, our method forecasts quite accurately for
the months of the 25-th year.

34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg