The paper of this project, named Segmented Pairwise Distance for Time Series with Large Discontinuities, was accepted by WCCI (IJCNN) 2020. SPD is orthogonal to distance-based algorithms and can be embedded in them. We validate advantages of SPD-embedded algorithms over corresponding distance-based ones on both open datasets and a proprietary dataset of surgical time series (of surgeons performing a temporal bone surgery in a virtual reality surgery simulator). Experimental results demonstrate that SPD-embedded algorithms outperform corresponding distance-based ones in distance measurement between time series with large discontinuities, measured by the Silhouette index (SI).
Matlab
DTW, CIDTW, DDTW, WDTW and WDDTW
SDTW, SCIDTW, SDDTW, SWDTW and SWDDTW
- Cortical mastoidectomy (CM) dataset in workspace_cm.mat
All algorithms (ten in total) are in SPD.m. They can use functions of SI.m, wdtw.m and weight.m to calculate SIs of every algorithm applied on the CM dataset.
Only Matlab codes implementing ten algorithms on the collected CM dataset is published. Readers can easily apply them to other datasets including the AG dataset and the IUM dataset.
This project is licensed under the MIT License - see the LICENSE file for details.