We obtained historical data of rainfall in Warri Town for the period 2003-2012 for the purpose of model identification and those of 2013 for forecast validation of the identified model. Model identification was by vis...We obtained historical data of rainfall in Warri Town for the period 2003-2012 for the purpose of model identification and those of 2013 for forecast validation of the identified model. Model identification was by visual inspection of both the sample ACF and sample PACF to postulate many possible models and then use the model selection criterion of Residual Sum of Square (RSS), Akaike’s Information Criterion (AIC) complemented by the Schwartz’s Bayesian Criterion (SBC), to choose the best model. The chosen model was the Seasonal ARIMA (1, 1, 1) (0, 1, 1) process which met the criterion of model parsimony with RSS value of 81.098,773, AIC value of 281.312,35 and SBC value of 289.330,84. Model adequacy checks showed that the model was appropriate. We used the model to forecast rainfall for 2013 and the result compared very well with the observed empirical data for 2013.展开更多
Gross Domestic Product(GDP)is the total market value of final goods and services produced by a country in a year.This study attempted to find the best-fit Autoregressive Integrated Moving Average(ARIMA)model for forec...Gross Domestic Product(GDP)is the total market value of final goods and services produced by a country in a year.This study attempted to find the best-fit Autoregressive Integrated Moving Average(ARIMA)model for forecasting China’s GDP over the next five years(2025 to 2029).In this study,we collected historical GDP data for China from 1960 to 2024 from the World Bank.Using the Box-Jenkins approach,we examined the Autocorrelation Function(ACF)and Partial Autocorrelation Function(PACF)plots,performed stationarity tests,and tested several models using the Akaike Information Criterion(AIC).We determined ARIMA(1,2,1)would be the best model to fit the data.We then used the fitted model to forecast the following five years for GDP in China,demonstrating the capabilities of ARIMA as an effective forecasting model.This study provides valuable insights for policymakers and economists in planning sustainable economic strategies for China's future development.展开更多
文摘We obtained historical data of rainfall in Warri Town for the period 2003-2012 for the purpose of model identification and those of 2013 for forecast validation of the identified model. Model identification was by visual inspection of both the sample ACF and sample PACF to postulate many possible models and then use the model selection criterion of Residual Sum of Square (RSS), Akaike’s Information Criterion (AIC) complemented by the Schwartz’s Bayesian Criterion (SBC), to choose the best model. The chosen model was the Seasonal ARIMA (1, 1, 1) (0, 1, 1) process which met the criterion of model parsimony with RSS value of 81.098,773, AIC value of 281.312,35 and SBC value of 289.330,84. Model adequacy checks showed that the model was appropriate. We used the model to forecast rainfall for 2013 and the result compared very well with the observed empirical data for 2013.
文摘Gross Domestic Product(GDP)is the total market value of final goods and services produced by a country in a year.This study attempted to find the best-fit Autoregressive Integrated Moving Average(ARIMA)model for forecasting China’s GDP over the next five years(2025 to 2029).In this study,we collected historical GDP data for China from 1960 to 2024 from the World Bank.Using the Box-Jenkins approach,we examined the Autocorrelation Function(ACF)and Partial Autocorrelation Function(PACF)plots,performed stationarity tests,and tested several models using the Akaike Information Criterion(AIC).We determined ARIMA(1,2,1)would be the best model to fit the data.We then used the fitted model to forecast the following five years for GDP in China,demonstrating the capabilities of ARIMA as an effective forecasting model.This study provides valuable insights for policymakers and economists in planning sustainable economic strategies for China's future development.
