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Univariate Time Series Analysis of Short‐Term Forecasting Horizons Using Artificial Neural Networks: The Case of Public Ambulance Emergency Preparedness

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  • Tichaona W. Mapuwei
  • Oliver Bodhlyera
  • Henry Mwambi

Abstract

This study examined the applicability of artificial neural network models in modelling univariate time series ambulance demand for short‐term forecasting horizons in Zimbabwe. Bulawayo City Councils’ ambulance services department was used as a case study. Two models, feed‐forward neural network (FFNN) and seasonal autoregressive integrated moving average, (SARIMA) were developed using monthly historical data from 2010 to 2017 and compared against observed data for 2018. The mean absolute error (MAE), root mean square error (RMSE), and paired sample t‐test were used as performance measures. Calculated performance measures for FFNN were MAE (94.0), RMSE (137.19), and the test statistic value p = 0.493(>0.05) whilst corresponding values for SARIMA were 105.71, 125.28, and p = 0.005(

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  • Tichaona W. Mapuwei & Oliver Bodhlyera & Henry Mwambi, 2020. "Univariate Time Series Analysis of Short‐Term Forecasting Horizons Using Artificial Neural Networks: The Case of Public Ambulance Emergency Preparedness," Journal of Applied Mathematics, John Wiley & Sons, vol. 2020(1).
    • Handle: RePEc:wly:jnljam:v:2020:y:2020:i:1:n:2408698
    DOI: 10.1155/2020/2408698
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