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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.01301 |
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| _version_ | 1866911296261193728 |
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| author | Mohan, Jeshwanth Ramsundar, Bharath Subramanian, Sandya |
| author_facet | Mohan, Jeshwanth Ramsundar, Bharath Subramanian, Sandya |
| contents | Modeling heterogeneous correlated time series requires the ability to learn hidden dynamic relationships between component time series with possibly varying periodicities and generative processes. To address this challenge, we formulate and evaluate a windowed variance-correlation metric (WVC) designed to quantify time-varying correlations between signals. This method directly recovers hidden relationships in an specified time interval as a weighted adjacency matrix, consequently inferring hidden dynamic graph structure. On simulated data, our method captures correlations that other methods miss. The proposed method expands the ability to learn dynamic graph structure between significantly different signals within a single cohesive dynamical graph model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01301 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inferring Dynamic Hidden Graph Structure in Heterogeneous Correlated Time Series Mohan, Jeshwanth Ramsundar, Bharath Subramanian, Sandya Methodology Signal Processing Modeling heterogeneous correlated time series requires the ability to learn hidden dynamic relationships between component time series with possibly varying periodicities and generative processes. To address this challenge, we formulate and evaluate a windowed variance-correlation metric (WVC) designed to quantify time-varying correlations between signals. This method directly recovers hidden relationships in an specified time interval as a weighted adjacency matrix, consequently inferring hidden dynamic graph structure. On simulated data, our method captures correlations that other methods miss. The proposed method expands the ability to learn dynamic graph structure between significantly different signals within a single cohesive dynamical graph model. |
| title | Inferring Dynamic Hidden Graph Structure in Heterogeneous Correlated Time Series |
| topic | Methodology Signal Processing |
| url | https://arxiv.org/abs/2512.01301 |