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Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis

机译:约旦降雨数据的时间序列分析模型:使用时间序列分析的案例研究

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Problem statement: Time series analysis and forecasting has become a major tool in different applications in hydrology and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). Approach: In this study we used Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken for Amman airport station for the period from 1922-1999 with a total of 936 readings. Results: In this research, ARIMA (1, 0, 0) (0, 1, 1)12 model was developed. This model is used to forecasting the monthly rainfall for the upcoming 10 years to help decision makers establish priorities in terms of water demand management. Conclusion/Recommendations: An intervention time series analysis could be used to forecast the peak values of rainfall data.
机译:问题陈述:时间序列分析和预测已成为水文学和环境管理领域不同应用中的主要工具。 Box和Jenkins引入的模型是ARIMA(自回归综合移动平均值),它是分析时间序列数据的最有效方法。方法:在这项研究中,我们使用Box-Jenkins方法建立了ARIMA模型,用于获取1922-1999年间安曼机场站的月降雨量数据,共936个读数。结果:在这项研究中,开发了ARIMA(1、0、0)(0、1、1)12模型。该模型用于预测未来十年的月降雨量,以帮助决策者确定用水需求管理方面的优先事项。结论/建议:干预时间序列分析可用于预测降雨数据的峰值。

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