Selected article for: "forecast model and seasonality trend"

Author: Pavan Kumar; Himangshu Kalita; Shashikanta Patairiya; Yagya Datt Sharma; Chintan Nanda; Meenu Rani; Jamal Rahmai; Akshaya Srikanth Bhagavathula
Title: Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020 through ARIMA Model with Machine Learning Approach
  • Document date: 2020_3_31
  • ID: lxakf79k_12
    Snippet: Furthermore, data smoothening was applied to stabilize the data by removing changes in the level of a time series, and therefore eliminating (or reducing) trend and seasonality. After this, the forecast prediction model was applied by using AR and MA models to generate plots of the different trends in upcoming days. The outcome of these predictions is presented in Figure 3 ......
    Document: Furthermore, data smoothening was applied to stabilize the data by removing changes in the level of a time series, and therefore eliminating (or reducing) trend and seasonality. After this, the forecast prediction model was applied by using AR and MA models to generate plots of the different trends in upcoming days. The outcome of these predictions is presented in Figure 3 .

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