Selected article for: "model parameter and parameter estimation"
Author: Tanujit Chakraborty; Indrajit Ghosh
Title: Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis
  • Document date: 2020_4_14
    • ID: ba6mdgq3_16
    Snippet: where y t denotes the actual value of the variable under consideration at time t, ε t is the random error at time t. The φ i and θ j are the coefficients of the ARIMA model. The basic assumption made by the ARIMA model is that the error series follows zero mean with constant variance, and satisfies the i.i.d condition. Building an ARIMA model for any given time series dataset can be described in three iterative steps: model identification (ach.....
    Document: where y t denotes the actual value of the variable under consideration at time t, ε t is the random error at time t. The φ i and θ j are the coefficients of the ARIMA model. The basic assumption made by the ARIMA model is that the error series follows zero mean with constant variance, and satisfies the i.i.d condition. Building an ARIMA model for any given time series dataset can be described in three iterative steps: model identification (achieving stationarity), parameter estimation (the autocorrelation function (ACF) and the partial autocorrelation function (PACF) plots are used to select the values of parameters p and q), and model diagnostics checking (finding the 'best' fitted forecasting model using Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)) [15] .

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