Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition

Understand

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.