无监督发现多变量时间序列中的潜在关系网络
Latent Structural Similarity Networks for Unsupervised Discovery in Multivariate Time Series
- 用自编码器学窗口级表示，再聚合为实体嵌入
- 通过稀疏阈值构建隐空间相似性网络，揭示潜在关联
- 适合探索性分析，不用于预测或决策
本文提出一种面向多变量时间序列的无任务发现层，可在不假设线性、平稳性或下游目标的前提下，对实体构建关系假设图。方法使用无监督序列到序列自编码器学习窗口级序列表示，将这些表示聚合为实体级嵌入，并通过阈值化隐空间相似性度量生成稀疏相似性网络。该网络作为可分析的抽象结构，压缩成对搜索空间，暴露候选关系以供进一步研究，而非用于预测、交易或其他决策规则。在具有挑战性的真实世界加密货币小时收益率数据集上进行验证，显示隐空间相似性可诱导出连贯的网络结构；同时采用经典计量经济学关系作为外部诊断工具，辅助解释发现的边。
原文摘要 · Abstract (English)
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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