Control Chart for Correcting the ARIMA Time Series Model of GDP Growth Cases

Nurfitri Imro'ah, N. M. Huda, Dewi Setyo Utami, Tarisa Umairah, Nani Fitria Arini
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Abstract

The essential prerequisite for attending the G20 conference is a country's GDP because G20 members can significantly boost the economy and preserve the nation's financial stability. Time series data can be thought of as a country's Gross Domestic Product (GDP) at a particular point in time. In this research, the GDP numbers from five Southeast Asian nations that are attending the G20 fulfilling are used. The total was 47 observations made yearly, which extended from 1975 to 2001. A time series analysis was performed on the data gathered. The correctness of time series models is also evaluated using control charts based on this research. The control chart is constructed using the time series model's residuals as observations. After applying the IMR control chart for verification, the results revealed that the residuals, specifically the models for GDP in Malaysia, Singapore, and Thailand, are out of control. The white noise assumption is fulfilled by the time series model obtained for Brunei and Indonesia's GDP, but the residuals are out of control. Whether controlled residuals are used depends on the accuracy with which the time series model predicts the future. If the amount of residuals is under control, then the time series model produced is accurate and good enough for prediction. After using the IMR control chart to verify the residuals, the results indicate that the residuals, namely the models for GDP in Malaysia, Singapore, and Thailand, are not under control. The assumption of white noise is proved correct by the time series model obtained for the GDP of Brunei Darussalam and Indonesia. With that being said, the residuals are entirely out of control. The model must improve its ability to forecast various future periods. It is a consequence of the unmanageable residuals that the model contains. Even if the best available model has been obtained based on the criteria that have been defined, it is anticipated that the research findings will improve the theories that have previously been developed and raise knowledge regarding the usefulness of testing the time series model. In addition to all of that, it is intended that the research will produce a summary of cases of an increase in GDP from five Southeast Asian countries participating in the G20 conference. 
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修正国内生产总值增长 ARIMA 时间序列模型案例的控制图
参加 G20 会议的基本前提是一个国家的国内生产总值,因为 G20 成员国可以极大地促进经济发展,维护国家的金融稳定。时间序列数据可视为一个国家在特定时间点的国内生产总值(GDP)。本研究使用了参加 G20 会议的五个东南亚国家的国内生产总值数据。从 1975 年到 2001 年,每年共进行 47 次观测。对收集到的数据进行了时间序列分析。在此研究基础上,还使用控制图评估了时间序列模型的正确性。控制图是以时间序列模型的残差作为观测值绘制的。在应用 IMR 控制图进行验证后,结果显示残差,特别是马来西亚、新加坡和泰国的国内生产总值模型失控。文莱和印度尼西亚国内生产总值的时间序列模型符合白噪声假设,但残差失控。是否使用受控残差取决于时间序列模型预测未来的准确性。如果残差量在可控范围内,那么所生成的时间序列模型就足够准确和适合预测。在使用 IMR 控制图验证残差后,结果表明马来西亚、新加坡和泰国的国内生产总值模型的残差没有得到控制。文莱达鲁萨兰国和印度尼西亚国内生产总值的时间序列模型证明白噪声假设是正确的。尽管如此,残差完全失控。该模型必须提高对未来各个时期的预测能力。这是模型残差无法控制的结果。即使根据已确定的标准获得了现有的最佳模型,预计研究结果也将改进以前提出的理论,并提高有关测试时间序列模型有用性的知识。除此以外,本研究还打算对参加 20 国集团会议的五个东南亚国家的国内生产总值增长案例进行总结。
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