论文标题
HDC-Minirocket：在时间序列分类中使用高维计算编码的显式时间
HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing
论文作者
论文摘要
时间序列数据的分类是许多应用程序域的重要任务。就准确性和计算时间而言，这项任务的现有方法之一是微型。在这项工作中，我们扩展了这种方法，以使用高维计算（HDC）机制提供更好的全局时间编码。 HDC（也称为矢量符号体系结构，VSA）是一种在高维矢量中显式表示和处理信息的通用方法。它以前已成功地与深层神经网络和其他信号处理算法相结合。我们认为，Minirocket的内部高维表示非常适合与HDC代数相辅相成。这导致了更通用的公式HDC-Minirocket，其中原始算法只是一种特殊情况。我们将讨论并证明HDC Minirocket可以系统地克服简单合成数据集上Minirocket的灾难性故障。这些结果通过来自UCR时间序列分类基准的128个数据集的实验证实。使用HDC的扩展可以在不增加计算工作的情况下，在具有高时间依赖性的数据集上取得更好的结果。
Classification of time series data is an important task for many application domains. One of the best existing methods for this task, in terms of accuracy and computation time, is MiniROCKET. In this work, we extend this approach to provide better global temporal encodings using hyperdimensional computing (HDC) mechanisms. HDC (also known as Vector Symbolic Architectures, VSA) is a general method to explicitly represent and process information in high-dimensional vectors. It has previously been used successfully in combination with deep neural networks and other signal processing algorithms. We argue that the internal high-dimensional representation of MiniROCKET is well suited to be complemented by the algebra of HDC. This leads to a more general formulation, HDC-MiniROCKET, where the original algorithm is only a special case. We will discuss and demonstrate that HDC-MiniROCKET can systematically overcome catastrophic failures of MiniROCKET on simple synthetic datasets. These results are confirmed by experiments on the 128 datasets from the UCR time series classification benchmark. The extension with HDC can achieve considerably better results on datasets with high temporal dependence without increasing the computational effort for inference.
