AN APPLICATION OF TIME INDEPENDENT FOURIER AMPLITUDE MODEL ON FORECASTING THE UNITED STATE POPULATION
DOI:
https://doi.org/10.33003/fjs-2022-0601-881Keywords:
Forecasting, Fourier Coefficients, Model, Population, TimeAbstract
This study applied the Time Independent Fourier Amplitude Model Approach to forecast the Population of the United States of America from 1790 to 2020 and beyond on a 10-year interval using Number Crunches Statistical software (NCSS). Results obtained using this methodology was compared with the results obtained in the other models: Malthusian, Logistics, and Logistics (Least Squares) Model. These models were compared using the goodness of fit (the coefficient of determination (R2) and the sum of square error (SSE)), the Akaike information criterion (AIC), Bayesian information criterion (BIC), Mean Absolute Deviation (MAD), Mean Error (ME), and Mean Sum of square Error (MSSE), Results displays that the Time Independent Fourier Amplitude Model and also is a suitable model for predicting the United States population
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