Using BPNN to Predict Number of Low-Income Households in Taiwan Based on Seasonally Adjusted Annualized Rates for Real GDP

Yi Ti Tung; Tzu Yi Pai

doi:10.4028/www.scientific.net/AMR.1044-1045.1824

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1044-1045Using BPNN to Predict Number of Low-Income...

Using BPNN to Predict Number of Low-Income Households in Taiwan Based on Seasonally Adjusted Annualized Rates for Real GDP

Abstract:

In this study, the back-propagation neural network (BPNN) was used to predict the number of low-income households (NLIH) in Taiwan, taking the seasonally adjusted annualized rates (SAAR) for real gross domestic product (GDP) as input variables. The results indicated that the lowest mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and highest correlation coefficient (R) for training and testing were 4.759 % versus 19.343 %, 24429972.268 versus 781839890.859, 4942.669 versus 27961.400, and 0.945 versus 0.838, respectively.

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Periodical:

Advanced Materials Research (Volumes 1044-1045)

Pages:

1824-1827

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1044-1045.1824

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Online since:

October 2014

Authors:

Yi Ti Tung, Tzu Yi Pai*

Keywords:

Back-Propagation Neural Network (BPNN), Demand of Rest of the World, Domestic Demand, GDP, Low-Income Households, Seasonally Adjusted Annualized Rates (SAAR)

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* - Corresponding Author

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