Time series modelling and forecasting for predicting Covid19 Case Load using LSTM

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Sellam V
Mohit Gorakhpuriya
Avani Mishra
Prince Kevadiya


The epidemic of the Novel Coronavirus across the globe has influenced the globe overall and caused a large number of death results. This remains as an unfavorable admonition to general wellbeing and will be set apart as perhaps the inordinate pandemics in the history. Inorder to validate and analyse, the details was taken from COVID-19. The detail contains daily tallies of confirmed, relieved and demise cases. Likewise, it includes extra data with respect to patients testing present in various states and the outcomes isolated in confirmed and invalidated cases. With the data provided it allows infected person to get the proper treatment and timely quarantine. The proposed paper utilizes Long Short Term Memory (LSTM ) networks for sequential prediction of data. The networks are viable apparatuses in short-term time series gauge the COVID-19 confirmed cases. It is a complex gated memory unit made to disappearing gradient issues restricting the effectiveness of a basic Recurrent Neural Network (RNN). Here Neural Network is used to solve the complex operation on the dataset. The result demonstrate that the LSTM Network is executed with various activation functions by utilizing a exponential linear unit brought about better execution for determining the complete number of COVID-19 cases. With the timely observations the corona virus state can be effectively monitored and the proper treatment can be assigned for the infected ones.

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How to Cite
S. V, M. Gorakhpuriya, A. Mishra, and P. Kevadiya, “Time series modelling and forecasting for predicting Covid19 Case Load using LSTM”, Int. J. Data. Science., vol. 2, no. 1, pp. 56-62, Sep. 2021.


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