Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic Energy Forecasting using Quantile Regressions based on a
  new Nearest Neighbors Quantile Filter   Parametric quantile regressions are a useful tool for creating probabilistic
energy forecasts. Nonetheless, since classical quantile regressions are trained
using a non-differentiable cost function, their creation using complex data
mining techniques (e.g., artificial neural networks) may be complicated. This
article presents a method that uses a new nearest neighbors quantile filter to
obtain quantile regressions independently of the utilized data mining technique
and without the non-differentiable cost function. Thereafter, a validation of
the presented method using the dataset of the Global Energy Forecasting
Competition of 2014 is undertaken. The results show that the presented method
is able to solve the competition's task with a similar accuracy and in a
similar time as the competition's winner, but requiring a much less powerful
computer. This property may be relevant in an online forecasting service for
which the fast computation of probabilistic forecasts using not so powerful
machines is required.

26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph