team_name: University of Sydney Forecast Lab model_name: One Model by Manifold Embedding model_abbr: USyd-OneModelMan model_contributors: Pablo Montero Manso (University of Sydney) 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."