缺少数据归算方法对单变血压时间序列数据分析和预测与ARIMA和LSTM的影响
Nicholas Niako1, Jesus D Melgarejo2,3,4, Gladys E Maestre2,3,5,4
1School of Mathematical & Statistical Sciences, University of Texas Rio Grande Valley, One West University Boulevard, Brownsville, TX, 78520, USA.
BMC medical research methodology
|December 26, 2024
概括
选择正确的归算方法对于准确的时间序列预测至关重要. 卡尔曼光滑和移动平均技术为自回归集成移动平均 (ARIMA) 和长短期记忆 (LSTM) 模型提供一致的性能.
科学领域:
- 时间序列分析时间序列分析
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 在单变时间序列中缺少数据会给分析带来挑战.
- 计算对于处理不完整的时间序列数据至关重要.
- 关于归算方法如何影响时间序列预测性能的研究有限.
研究的目的:
- 评估十种不同的归算技术对ARIMA和LSTM模型预测性能的影响.
- 为了比较单变量时间序列数据的各种归算方法的有效性.
- 确定时间序列预测的最佳归算策略.
主要方法:
- 在MCAR下使用24小时透气血压读数生成了10-35%的缺失数据.
- 应用了十种归算技术:平均值,卡尔曼过,线性,线条,斯坦曼,EWMA,SMA,KNN和LOCF.
- 将ARIMA和LSTM模型的预测性能与原始数据相比较.
主要成果:
- 计算技术改变了数据的自相关性,影响了ARIMA和LSTM的预测.
- 没有单一的归算方法可以保证卓越的预测.
- 平均,LOCF,KNN,斯坦曼和立方脊柱表现良好,缺失率低.
- 在所有失踪级别中,EWMA和卡尔曼过显示出一致的性能.
- 在预测准确度方面,LSTM略高于ARIMA,尽管结果各不相同.
- 在小样本中,ARIMA在高自相关性数据上表现更好.
结论:
- 推使用卡尔曼光滑,插值 (线性,斜线,斯坦曼) 和移动平均 (SMA,EWMA) 技术来计算单变量时间序列.
- 这些方法在ARIMA和LSTM模型的数据分布和预测方面表现良好.
- 与ARIMA相比,LSTM的预测准确度略高,但ARIMA对于小数据集来说更简单.
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