Abstract

When time series are processed, the difficulty increases with the size of the series. This fact is aggravated when time series are processed online, since their size increases indefinitely. Therefore, reducing their number of points, without significant loss of information, is an important field of research. This article proposes an optimal online segmentation method, called OSFS-OnL, which guarantees that the number of segments is minimal, that a preset error limit is not exceeded using the \(L \infty \) -norm, and that for that number of segments the value of the error corresponding to the \(L^2\) -norm is minimized. This new proposal has been compared with the optimal OSFS offline segmentation method and has shown better computational performance, regardless of its flexibility to apply it to online or offline segmentation.

中文翻译：

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最优在线时间序列分割

摘要

处理时间序列时，难度随着序列的大小而增加。当在线处理时间序列时，这一事实会更加严重，因为它们的大小会无限增加。因此，在不显着丢失信息的情况下减少点的数量是一个重要的研究领域。本文提出了一种称为 OSFS-OnL 的最佳在线分段方法，该方法保证分段数量最少，使用\(L \infty \)范数保证不超过预设的错误限制，并且对于该数量的分段分段对应于\(L^2\)范数的误差值被最小化。这一新提议与最佳 OSFS 离线分割方法进行了比较，无论其是否灵活地应用于在线或离线分割，都显示出更好的计算性能。