论文标题
ABBA：基于自适应的布朗桥的符号聚合时间序列
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series
论文作者
论文摘要
引入了一个名为ABBA的新符号表示。它基于时间序列的自适应多边形链近似为一系列元素，然后进行基于平均的聚类以获得符号表示。我们表明，该表示形式的重建误差可以建模为带有固定起点和终点的随机步行，即所谓的布朗桥。这种见解使我们能够使ABBA基本上无参数，除了必须选择的近似值。与SAX和1D-SAX表示形式进行了广泛的比较形式，表明ABBA能够更好地保留与其他方法相比的时间序列的基本形状信息。讨论了ABBA的优势和应用，包括其内部差异属性和用于异常检测的使用以及提供的Python实施。
A new symbolic representation of time series, called ABBA, is introduced. It is based on an adaptive polygonal chain approximation of the time series into a sequence of tuples, followed by a mean-based clustering to obtain the symbolic representation. We show that the reconstruction error of this representation can be modelled as a random walk with pinned start and end points, a so-called Brownian bridge. This insight allows us to make ABBA essentially parameter-free, except for the approximation tolerance which must be chosen. Extensive comparisons with the SAX and 1d-SAX representations are included in the form of performance profiles, showing that ABBA is able to better preserve the essential shape information of time series compared to other approaches. Advantages and applications of ABBA are discussed, including its in-built differencing property and use for anomaly detection, and Python implementations provided.
