Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Resilient Neural Forecasting Systems   Industrial machine learning systems face data challenges that are often
under-explored in the academic literature. Common data challenges are data
distribution shifts, missing values and anomalies. In this paper, we discuss
data challenges and solutions in the context of a Neural Forecasting
application on labor planning.We discuss how to make this forecasting system
resilient to these data challenges. We address changes in data distribution
with a periodic retraining scheme and discuss the critical importance of model
stability in this setting. Furthermore, we show how our deep learning model
deals with missing values natively without requiring imputation. Finally, we
describe how we detect anomalies in the input data and mitigate their effect
before they impact the forecasts. This results in a fully autonomous
forecasting system that compares favorably to a hybrid system consisting of the
algorithm and human overrides.

15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an