Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A unified machine learning approach to time series forecasting applied
  to demand at emergency departments   There were 25.6 million attendances at Emergency Departments (EDs) in England
in 2019 corresponding to an increase of 12 million attendances over the past
ten years. The steadily rising demand at EDs creates a constant challenge to
provide adequate quality of care while maintaining standards and productivity.
Managing hospital demand effectively requires an adequate knowledge of the
future rate of admission. Using 8 years of electronic admissions data from two
major acute care hospitals in London, we develop a novel ensemble methodology
that combines the outcomes of the best performing time series and machine
learning approaches in order to make highly accurate forecasts of demand, 1, 3
and 7 days in the future. Both hospitals face an average daily demand of 208
and 106 attendances respectively and experience considerable volatility around
this mean. However, our approach is able to predict attendances at these
emergency departments one day in advance up to a mean absolute error of +/- 14
and +/- 10 patients corresponding to a mean absolute percentage error of 6.8%
and 8.6% respectively. Our analysis compares machine learning algorithms to
more traditional linear models. We find that linear models often outperform
machine learning methods and that the quality of our predictions for any of the
forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. In
addition to comparing and combining state-of-the-art forecasting methods to
predict hospital demand, we consider two different hyperparameter tuning
methods, enabling a faster deployment of our models without compromising
performance. We believe our framework can readily be used to forecast a wide
range of policy relevant indicators.

35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph