找跨序列的相似变长子序列，还能比旧方法快20倍。
Inferring the Most Similar Variable-length Subsequences between Multidimensional Time Series
- 基于理论保证的精确算法，处理长度不一的时间序列对。
- 仿真数据中运行时间仅为基线四分之一，真实数据快20倍。
- 适合金融、动物行为等多维时序分析，代码开源可复用。
在多维时间序列中寻找最相似的变长子序列具有广泛应用：如捕捉股市依赖关系或发现狒狒群体协同运动模式。当一个时间序列中存在某种模式时，人们常关心该模式是否在另一序列中以不同长度的变形形式出现。然而，据我们所知，目前尚无高效框架解决此问题。本文提出一种算法，可精确求解长度不一致的时间序列间最相似的多维子序列，其理论基础确保了正确性与效率。仿真数据表明，本方法不仅结果准确，且运行时间仅为基线方法的四分之一；在真实数据中，速度提升高达20倍，并揭示了股市动态及狒狒多维运动序列间的关联。该方法适用于任意时间序列，相关代码与数据集已公开共享。
原文摘要 · Abstract (English)
Finding the most similar subsequences between two multidimensional time series has many applications: e.g. capturing dependency in stock market or discovering coordinated movement of baboons. Considering one pattern occurring in one time series, we might be wondering whether the same pattern occurs in another time series with some distortion that might have a different length. Nevertheless, to the best of our knowledge, there is no efficient framework that deals with this problem yet. In this work, we propose an algorithm that provides the exact solution of finding the most similar multidimensional subsequences between time series where there is a difference in length both between time series and between subsequences. The algorithm is built based on theoretical guarantee of correctness and efficiency. The result in simulation datasets illustrated that our approach not just only provided correct solution, but it also utilized running time only quarter of time compared against the baseline approaches. In real-world datasets, it extracted the most similar subsequences even faster (up to 20 times faster against baseline methods) and provided insights regarding the situation in stock market and following relations of multidimensional time series of baboon movement. Our approach can be used for any time series. The code and datasets of this work are provided for the public use.
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