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COVID-19 has been declared as the pandemic and is spreading at an alarming rate. The current study describes the situation outbreak in India. COVID-19 is a time-series data set which contains both non-linear and non-stationary patterns, and it is highly recommended to use the model which can extract the design using sequential networks. This study mainly focuses on the minimum number of hospital beds required for COVID-19 patients along with the prediction of confirmed covid-19 cases and total predicted deaths due to prevailing pandemic till 4 February 2021 using multi-time series forecasting models: (Facebook) Fb Prophet Model and Auto-Regressive Integrated Moving Average Model (ARIMA). The performances of the two models are compared on the basis of error metrics: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The study also uses exploratory data analysis to report the current situation of covid-19 in India .The data used in the study is taken from datasets available on Kaggle.com and covers up time period till 6 December 2020. All the data visualization, analysis and prediction are made using Python 3 in Jupyter notebook. The performed study can help government and healthcare communities to initiate appropriate measures to control this outbreak in India.

COVID-19 India Outbreak Trend Analysis and Predictions using Multi Time Series Models