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A disjoint pair-point exponential and time-weighted-average fire accident forecasting model for partial information availability situations

机译:局部信息可得情况的不相交对点指数和时间加权平均火灾事故预测模型

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摘要

Often, industries are confronted with incomplete information for forecasting decisions. Unfortunately, very few models can serve this purpose. In addressing this gap, a univariate model for random and fluctuating fire accidents is presented. A data turbulence monitoring mechanism - information turbulence index (ITI) - and an out-of-sample analysis-based forecasting tool - the disjoint pair-point exponential and time-weighted-average (DPEWTA) model - characterise the model. Comparative analysis of the model's performance against that of other forecasting models was investigated. Simulated outcomes indicate DPEWTA's relative superior and inferior forecast capabilities up to 22 and 5%, respectively, over ARIMA, ESM and MA at low-to-medium ITI values. Real system DPEWTA forecasts show MAPE and MAE values with the best prediction ranges obtained for an ITI of 0.20 to 0.25. Obtained results show the model's capability and dependability as a fire accident forecasting tool.
机译:通常,行业面临着用于预测决策的不完整信息。不幸的是,很少有模型可以达到这个目的。为了解决这一差距,提出了随机和波动火灾事故的单变量模型。数据湍流监视机制-信息湍流指数(ITI)-和基于样本外分析的预测工具-不相交对点指数和时间加权平均(DPEWTA)模型-对该模型进行了描述。对该模型的性能与其他预测模型的性能进行了比较分析。模拟结果表明,在中低ITI值下,DPEWTA的相对优缺点分别比ARIMA,ESM和MA分别高22和5%。实际系统的DPEWTA预测显示了MAPE和MAE值,其ITI为0.20到0.25时具有最佳预测范围。所得结果表明该模型作为火灾事故预测工具的能力和可靠性。

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