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渐进式有限误差 逐步线性近似与分辨率减小用于时间序列数据压缩
Jeng-Wei Lin1, Shih-Wei Liao2, Yu-Hung Tsai1
1Department of Information Management, Tunghai University, Taichung 407224, Taiwan.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了PBEPLA-RR,这是一种压缩来自AIoT设备的时间序列数据的新方法. 它能高效地产生多个近似值,精度不同,大大降低了存储需求和能源消耗.
科学领域:
- 数据科学数据科学数据科学
- 事物的人工智能 (AIoT)
- 数据压缩数据压缩
背景情况:
- 人工智能设备产生大量的时间序列数据,导致传输,存储和处理的高能源成本.
- 现有的损耗压缩方法通过牺牲准确性提供更好的比率,但不同的应用需要不同的数据保真度.
- 存储多个压缩版本用于不同的错误极限是低效的.
研究的目的:
- 开发一种方法,从单个压缩表示中高效地生成多个时间序列近似,具有不同的误差极限.
- 为了减少与存储和管理时间序列数据相关的整体数据大小和能源消耗.
- 动态提供根据特定准确性要求定制的数据版本.
主要方法:
- 时间序列逐渐分解成零碎的线性函数,从最大的误差边界开始.
- 使用Swing-Recursive Residual (Swing-RR) 算法,在每个分解步骤中生成有限误差逐步线性近似 (BEPLA).
- 聚合多个BEPLA以创建相继较小的误差界限的近似值.
主要成果:
- 拟议的方法PBEPLA-RR在8个现实数据集上进行了评估,其误差极限为5%,1%和0.5%.
- 多个BEPLA的组合数据大小与存储单个版本在最小误差边界的数据大小相当.
- 这大大减少了存储需求,而不是为每个错误界限保持独立的压缩版本.
结论:
- PBEPLA-RR有效地实现了时间序列数据的高压缩比.
- 该方法提供了一种灵活的方式,可以从单个压缩模型中获得具有不同误差极限的多个近似值.
- 这种方法为AIoT应用提供了大量的能源和存储节约.
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