Inferring the Most Similar Variable-length Subsequences between Multidimensional Time Series

• URL: http://arxiv.org/abs/2505.11106v1
• Date: Fri, 16 May 2025 10:39:46 GMT
• Title: Inferring the Most Similar Variable-length Subsequences between Multidimensional Time Series
• Authors: Thanadej Rattanakornphan, Piyanon Charoenpoonpanich, Chainarong Amornbunchornvej,
• Abstract summary: We propose an algorithm that provides the exact solution of finding the most similar multidimensional subsequences between time series.<n>The algorithm is built based on theoretical guarantee of correctness and efficiency.<n>In real-world datasets, it extracted the most similar subsequences even faster.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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