team_name: University of Sydney Forecast Lab
model_name: One Model by Manifold Embedding
model_abbr: USyd-OneModelMan
model_contributors:
  - name: Pablo Montero Manso
    affiliation: University of Sydney
    email: pablo.monteromanso@sydney.edu.au
website_url: https://github.com/pmontman/covid19forec
license: cc-by-4.0
team_model_designation: primary
methods: A single autoregressive model fit jointly to all European time series, adding
  time series from the top regions across the world. A high-dimensional manifold embedding
  is used capture the process.
data_inputs: JHU (reported fatalities)
citation: https://arxiv.org/abs/2008.00444
methods_long: The information of multiple time series can be shared in a single model
  via a large dimensional manifold embedding. In addition to Europe death series,
  the regions with the largest average daily deaths are added to reduce the variance  of
  the model estimation and share information (the regions more advanced in the pandemic
  can help forecast the others). Each time series is time-delay embedded and stacked
  together before for fitting a single linear autoregressive model. The dimension
  of the embedding is tuned by temporal validation, the best dimension of the last
  4 weeks. This methodology has been successfully applied in the ensemble forecast
  efforts of Spain and Australia.  See citation for detailed description and statistical
  properties.