  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.
