Selected article for: "ARIMA model residual and oscillatory series"
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_22
    Snippet: For the COVID-19 datasets, we propose a hybridization of stationary ARIMA and nonstationary WBF model to reduce the individual biases of the component models [24] . The COVID-19 cases datasets for five different countries are complex in nature. Thus, the ARIMA model fails to produce random errors or even nonstationary residual series, evident from Figure 1 . The behavior of the residual series generated by ARIMA is mostly oscillatory and periodic.....
    Document: For the COVID-19 datasets, we propose a hybridization of stationary ARIMA and nonstationary WBF model to reduce the individual biases of the component models [24] . The COVID-19 cases datasets for five different countries are complex in nature. Thus, the ARIMA model fails to produce random errors or even nonstationary residual series, evident from Figure 1 . The behavior of the residual series generated by ARIMA is mostly oscillatory and periodic; thus, we choose the wavelet function to model the remaining series. Several hybrid models based on ARIMA and neural networks are available in the field of time series forecasting; see for example [35; 1; 12; 19; 8; 25] .

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