Latent Structural Similarity Networks for Unsupervised Discovery in Multivariate Time Series
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908790200205312 |
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| author | Owoeye, Olusegun |
| author_facet | Owoeye, Olusegun |
| contents | This paper proposes a task-agnostic discovery layer for multivariate time series that constructs a relational hypothesis graph over entities without assuming linearity, stationarity, or a downstream objective. The method learns window-level sequence representations using an unsupervised sequence-to-sequence autoencoder, aggregates these representations into entity-level embeddings, and induces a sparse similarity network by thresholding a latent-space similarity measure. This network is intended as an analyzable abstraction that compresses the pairwise search space and exposes candidate relationships for further investigation, rather than as a model optimized for prediction, trading, or any decision rule. The framework is demonstrated on a challenging real-world dataset of hourly cryptocurrency returns, illustrating how latent similarity induces coherent network structure; a classical econometric relation is also reported as an external diagnostic lens to contextualize discovered edges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18803 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Latent Structural Similarity Networks for Unsupervised Discovery in Multivariate Time Series Owoeye, Olusegun Machine Learning This paper proposes a task-agnostic discovery layer for multivariate time series that constructs a relational hypothesis graph over entities without assuming linearity, stationarity, or a downstream objective. The method learns window-level sequence representations using an unsupervised sequence-to-sequence autoencoder, aggregates these representations into entity-level embeddings, and induces a sparse similarity network by thresholding a latent-space similarity measure. This network is intended as an analyzable abstraction that compresses the pairwise search space and exposes candidate relationships for further investigation, rather than as a model optimized for prediction, trading, or any decision rule. The framework is demonstrated on a challenging real-world dataset of hourly cryptocurrency returns, illustrating how latent similarity induces coherent network structure; a classical econometric relation is also reported as an external diagnostic lens to contextualize discovered edges. |
| title | Latent Structural Similarity Networks for Unsupervised Discovery in Multivariate Time Series |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.18803 |