Efficient High-Resolution Time Series Classification via Attention
  Kronecker Decomposition
Aosong Feng Jialin Chen Juan Garza Brooklyn Berry Francisco Salazar Yifeng Gao Rex Ying Leandros Tassiulas
The high-resolution time series classification problem is essential due to
the increasing availability of detailed temporal data in various domains. To
tackle this challenge effectively, it is imperative that the state-of-the-art
attention model is scalable to accommodate the growing sequence lengths
typically encountered in high-resolution time series data, while also
demonstrating robustness in handling the inherent noise prevalent in such
datasets. To address this, we propose to hierarchically encode the long time
series into multiple levels based on the interaction ranges. By capturing
relationships at different levels, we can build more robust, expressive, and
efficient models that are capable of capturing both short-term fluctuations and
long-term trends in the data. We then propose a new time series transformer
backbone (KronTime) by introducing Kronecker-decomposed attention to process
such multi-level time series, which sequentially calculates attention from the
lower level to the upper level. Experiments on four long time series datasets
demonstrate superior classification results with improved efficiency compared
to baseline methods.