Usage of specific attention improves change point detection
This work addresses change point detection for sequential data analysis, representing an incremental improvement over existing methods.
The paper tackled the problem of change point detection in sequential data by proposing a specific form of attention mechanism, which outperformed state-of-the-art results.
The change point is a moment of an abrupt alteration in the data distribution. Current methods for change point detection are based on recurrent neural methods suitable for sequential data. However, recent works show that transformers based on attention mechanisms perform better than standard recurrent models for many tasks. The most benefit is noticeable in the case of longer sequences. In this paper, we investigate different attentions for the change point detection task and proposed specific form of attention related to the task at hand. We show that using a special form of attention outperforms state-of-the-art results.