Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Collection of Historical Weather Data: Issues with Missing Values   Weather data collected from automated weather stations have become a crucial
component for making decisions in agriculture and in forestry. Over time,
weather stations may become out-of-order or stopped for maintenance, and
therefore, during those periods, the data values will be missing.
Unfortunately, this will cause huge problems when analysing the data. The main
aim of this study is to create high-quality historical weather datasets by
dealing efficiently with missing values. In this paper, we present a set of
missing data imputation methods and study their effectiveness. These methods
were used based on different types of missing values. The experimental results
show that two the proposed methods are very promising and can be used at larger
scale.

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The categories of this paper are: patt-sol
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex