BILCO: An Efficient Algorithm for Joint Alignment of Time Series
Xuelong Mi, Mengfan Wang, Alex Bo-Yuan Chen, Jing-Xuan Lim, Yizhi Wang, Misha B. Ahrens, Guoqiang Yu
摘要
Multiple time series data occur in many real applications and the alignment among them is usually a fundamental step of data analysis. Frequently, these multiple time series are inter-dependent, which provides extra information for the alignment task and this information cannot be well utilized in the conventional pairwise alignment methods. Recently, the joint alignment was modeled as a max-flow problem, in which both the profile similarity between the aligned time series and the distance between adjacent warping functions are jointly optimized. However, despite the new model having elegant mathematical formulation and superior alignment accuracy, the long computation time and large memory usage, due to the use of the existing general-purpose max-flow algorithms, limit significantly its well-deserved wide use. In this report, we present BIdirectional pushing with Linear Component Operations (BILCO), a novel algorithm that solves the joint alignment max-flow problems efficiently and exactly. We develop the strategy of linear component operations that integrates dynamic programming technique and the push-relabel approach. This strategy is motivated by the fact that the joint alignment maxflow problem is a generalization of dynamic time warping (DTW) and numerous individual DTW problems are embedded. Further, a bidirectional-pushing strategy is proposed to introduce prior knowledge and reduce unnecessary computation, by leveraging another fact that good initialization can be easily computed for the joint alignment max-flow problem. We demonstrate the efficiency of BILCO using both synthetic and real experiments. Tested on thousands of datasets under various simulated scenarios and in three distinct application categories, BILCO consistently achieves at least 10 and averagely 20-folds increase in speed, and uses at most 1/8 and averagely 1/10 memory compared with the best existing max-flow method. Our source code can be found at https://github.com/yu-lab-vt/BILCO .
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
相关 Paper
- Scaling Subsequence Similarity Join Based on Dynamic Time WarpingZemin Chao, Qiaoyi Zheng, Xingxing Xiao, Boyu Xiao 等ICDE 2026
- Parameter-free Spikelet: Discovering Different Length and Warped Time Series Motifs using an Adaptive Time Series RepresentationMakoto Imamura, Takaaki NakamuraKDD 2023 · 被引用 6 次
- Faster Multi-Object Segmentation using Parallel Quadratic Pseudo-Boolean OptimizationNiels Jeppesen, Patrick M. Jensen, Anders Nymark Christensen, Anders B. Dahl 等ICCV 2021 · 被引用 4 次
- TiVy: Time Series Visual Summary for Scalable VisualizationGromit Yeuk-Yin Chan, Luis Gustavo Nonato, Themis Palpanas, Cláudio T. Silva 等IEEE VIS 2025 · 被引用 1 次
- TokenTiming: A Dynamic Alignment Method for Universal Speculative Decoding Model PairsSibo Xiao, Jinyuan Fu, Zhongle Xie, Lidan ShouACL 2026
