Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study

Abstract

Highlights Deep Learning based time series forecasting and comparative case study of Covid-19 confirmed and death cases in India and USA. Recurrent neural network (RNN) based variants of long short term memory (LSTM) are being used to design proposed models. Convolutional LSTM based model outperform other models with high accuracy and very less error. One of the unique studies providing state-of-the-art results to help both countries to recede Covid-19 impact.

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods

“…Shastri et al. [13] performed a time series forecasting of Covid-19 using deep learning models for India and USA. Feroze [7] forecasted the patterns of COVID-19 using bayesian structural time series models.…”

What Can COVID-19 Teach Us about Using AI in Pandemics?

“…Soon, other AI engines were forecasting the dynamics of outbreaks in Saudi Arabia, Egypt, Brazil, Canada, India, USA, and African countries [ 26 , 33 , 34 , 35 , 36 ]. The most popular approach in AI design incorporates a long-term short memory-based AI engine utilizing rolling training sets [ 26 , 33 , 37 , 38 , 39 ]. Others used advanced autoregressive integrated moving average [ 18 , 35 , 40 ].…”

Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

“…Temporal forecasting of COVID-19 infection cases have been evaluated using multiple deep learning models. A multi-step prediction using the neural network models - Long Short Term Memory (LSTM), Convolutional LSTM (ConvLSTM), Bidirectional LSTM (Bi-LSTM)- as a case study for Canada, 16 different states in India, 17 and USA 18 have been studied. The Bi-LSTM model and the ConvLSTM models are shown to outperform the other LSTM variants with a Mean Absolute Percentage Error (MAPE) of 3% for day ahead predictions.…”