有效地使用内存数据来赋值单变量时间序列数据
Jay Darji1, Nupur Biswas1,2, Vijay Padul1
1Rhenix Lifesciences, Hyderabad, Telangana, India.
Frontiers in big data
|September 5, 2024
概括
使用内存数据输入缺失的时间序列数据可以显著减少错误,特别是在更长的间隙中. 这种方法提高了生物和非生物数据集的准确性.
科学领域:
- 数据科学数据科学数据科学
- 信号处理 信号处理
- 生物医学信息学 生物医学信息学
背景情况:
- 时间序列数据在许多行业中普遍存在,但经常存在缺失的值,影响分析.
- 现有的归算算法表现不同,受数据特征和缺失数据模式的影响.
研究的目的:
- 调查使用内置时间序列数据进行归算的有效性.
- 通过使用心率和功耗数据,在不同的缺失数据跨度和算法中评估归算性能.
主要方法:
- 在时间序列数据集 (心率,功耗) 中生成不同持续时间的缺失数据.
- 使用不同的算法对不同大小的内存数据计算缺失数据.
- 使用根平均平方误差 (RMSE) 度量来评估归算性能.
主要成果:
- 与使用整个数据集相比,使用封装数据减少了RMSE,特别是使用预期最大化 (EM) 算法.
- 对于1分钟,5分钟和15分钟的缺失数据间隔,观察到RMSE的减少,对于更长的数据间隔,有更大的改善.
- 数据波动和采样频率影响了数据归算的有效性.
结论:
- 用于归算的bined数据的实用性取决于缺失的数据跨度,数据采样频率和固有的数据波动.
- 捆绑数据归算对各种数据集有效,包括物联网衍生生物数据 (智能手表心率) 和非生物数据 (功耗).
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