Grey forecasting models based on internal optimization for Novel Corona virus (COVID-19)

Abstract

Pandemic forecasting has become an uphill task for the researchers on account of the paucity of sufficient data in the present times. The world is fighting with the Novel Coronavirus to save human life. In a bid to extend help to the concerned authorities, forecasting engines are invaluable assets. Considering this fact, the presented work is a proposal of two Internally Optimized Grey Prediction Models (IOGMs). These models are based on the modification of the conventional Grey Forecasting model (GM(1,1)). The IOGMs are formed by stacking infected case data with diverse overlap periods for forecasting pandemic spread at different locations in India. First, IOGM is tested using time series data. Its two models are then employed for forecasting the pandemic spread in three large Indian states namely, Rajasthan, Gujarat, Maharashtra and union territory Delhi. Several test runs are carried out to evaluate the performance of proposed grey models and conventional grey models GM(1,1) and NGM(1,1,k). It is observed that the prediction accuracies of the proposed models are satisfactory and the forecasted results align with the mean infected cases. Investigations based on the evaluation of error indices indicate that the model with a higher overlap period provides better results.

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

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