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arXiv · 2601.18803

Latent Structural Similarity Networks for Unsupervised Discovery in Multivariate Time Series

Olusegun Owoeye
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Abstract

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.

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Olusegun Owoeye. 2026-01-15. Latent Structural Similarity Networks for Unsupervised Discovery in Multivariate Time Series. https://arxiv.org/abs/2601.18803

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