Empirical Study of Weight Initializations for COVID-19 Predictions in India

Abstract

The first case of the novel Coronavirus disease (COVID-19) in India was recorded on 30th January 2020 in Kerela and it has spread across all states in India. The prediction of the number of COVID-19 cases is important for government officials to plan various control strategies. This paper presents a weekly prediction of cumulative number of COVID-19 cases in India. A graded lockdown feature, which describes the status of lockdown, is derived and incorporated in the input dataset as one of the features. For prediction, this paper proposes a model which is a stacking of different deep neural networks which have recurrent connections. Vanishing gradients is a common issue with such networks with recurrent connections. Proper weight initialization of the network is one of the solutions to overcome the vanishing gradients problem. Hence, the weight distributions and convergence performance of some state-of-the-art weight initialization techniques have been analyzed in this paper. The proposed model is initialized with the technique which would aid to avoid the vanishing gradients problem and converge faster to a lower loss. This paper also provides a comparison of the proposed model for univariate and multivariate prediction with other prediction models such as statistical model - Auto-Regressive Integrated Moving Average (ARIMA), and deep learning architectures long short term memory (LSTM), bidirectional LSTM (bi-LSTM) and gated recurrent unit (GRU). The results demonstrate that the proposed model gives better prediction results than these models.