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An Adaptive Control Combination Forecasting Method for Time Series Data

机译:时间序列数据的自适应控制组合预测方法

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According to the individual forecasting methods, an adaptive control combination forecasting (ACCF) method with adaptive weighting coefficients was proposed for short-term prediction of the time series data. The US population dataset, the American electric power dataset, and the vibration signal dataset in a hydraulic test rig were separately tested by using ACCF method, and then, the accuracy analysis of ACCF method was carried out in the study. The results showed that, in contrast to individual methods or combination methods, the proposed ACCF method was adaptive to adopt one or some of prediction methods and showed satisfactory forecasting results due to flexible adaptability and a high accuracy. It was also concluded that the higher the noise ratio of the tested datasets, the lower the prediction accuracy of the ACCF method; the ACCF method demonstrated a better prediction trend with good volatility and following quality under noisy data, as compared with other methods.
机译:根据各种预测方法,提出了一种具有自适应加权系数的自适应控制组合预测(ACCF)方法,用于时间序列数据的短期预测。 使用ACCF方法单独测试美国群体数据集,美国电力数据集和液压试验台中的振动信号数据集,然后在研究中进行ACCF方法的精度分析。 结果表明,与单独的方法或组合方法相比,所提出的ACCF方法是自适应的,采用一种或一些预测方法,并且由于灵活的适应性和高精度而显示出令人满意的预测结果。 还得出结论,测试数据集的噪声比越高,ACCF方法的预测精度越低; 与其他方法相比,ACCF方法展示了具有良好波动性和噪声数据的质量良好的预测趋势。

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