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    Area of Science:

    • Statistics
    • Econometrics
    • Biostatistics

    Background:

    • Traditional time series analysis involves a two-stage process: ARIMA model identification and data transformation.
    • This conventional method requires a substantial number of data points for accurate model identification.
    • Existing approaches may limit the applicability of time series analysis in certain scenarios.

    Purpose of the Study:

    • To introduce a generalized transformation approach for time series analysis.
    • To eliminate the necessity of the model identification step in ARIMA modeling.
    • To enhance the flexibility and efficiency of analyzing repeated observations.

    Main Methods:

    • A generalized transformation is employed to bypass the ARIMA model identification stage.
    • The approach utilizes a generalized least squares algorithm for computational procedures.
    • This method relaxes the requirement for a large number of data points for model identification.

    Main Results:

    • The generalized transformation approach simplifies the analysis of time series data.
    • It expands the applicability of time series analysis to a wider range of situations.
    • The method offers greater computational flexibility and efficiency compared to traditional approaches.

    Conclusions:

    • The generalized transformation offers a more accessible and efficient method for time series analysis.
    • This approach broadens the utility of time series analysis, particularly when data points are limited.
    • The study highlights the potential of generalized least squares for robust time series intervention analysis.