Author: Pavan Kumar; Ram Kumar Singh; Chintan Nanda; Himangshu Kalita; Shashikanta Patairiya; Yagya Datt Sharma; Meenu Rani; Akshaya Srikanth Bhagavathula
Title: Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models Document date: 2020_4_2
ID: b9p5tqhl_15
Snippet: A stationary time series where data properties do not depend on the time at which the series is observed. Therefore, time-series data with trends or with seasonality are not stationary as it will affect the value of the data at different times. In our study here, we are using machine learning tools for predicting the spread of COVID-19 in the future, so having a stationary time series data is very important for further predictable modeling. As we.....
Document: A stationary time series where data properties do not depend on the time at which the series is observed. Therefore, time-series data with trends or with seasonality are not stationary as it will affect the value of the data at different times. In our study here, we are using machine learning tools for predicting the spread of COVID-19 in the future, so having a stationary time series data is very important for further predictable modeling. As we can see, the trend is followed by the variables used in our data for the victims affected by COVID-19. Therefore, to test whether the data is stationary or not becomes a very vital aspect of our research.
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