Selected article for: "absolute percent and accuracy metric"
Author: swarna kamal paul; Saikat Jana; Parama Bhaumik
Title: A multivariate spatiotemporal spread model of COVID-19 using ensemble of ConvLSTM networks
  • Document date: 2020_4_22
    • ID: nng76upj_14
    Snippet: . CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is output of the ensemble, is output of the model , is set of all models in the ensemble, is number of infected patients in the training samples of model , is mean squared error of model on validation dataset and is softmax function. The weights are proportional to the amount of positive cases used.....
    Document: . CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is output of the ensemble, is output of the model , is set of all models in the ensemble, is number of infected patients in the training samples of model , is mean squared error of model on validation dataset and is softmax function. The weights are proportional to the amount of positive cases used for training the model and inversely proportional to the validation error. During testing the model is given a sequence of most recent frames as input and the next frame is predicted. The predicted frame is temporally appended to the input sequence of frames and fed to the model again to obtain the next predicted frame. This continues until required number of future frames are predicted. The accuracy of a model is tested with the metric "mean absolute percent error" (MAPE) and Kullback-Liebler (KL) divergence [17] .

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