SCENTS: multi-source streaming consensus embedding for time series data fusion

This paper proposes SCENTS, a Streaming Consensus Embedding for Time Series that fuses streaming windows into a single low-dimensional representation suitable for diverse downstream tasks. First, it learns a denoised low-dimensional latent basis for state initialisation ( Z ∈ R N × d ). Second, for each newly arriving stream, it performs a near-linear time, multi-pass consensus update that fuses new affinities directly into Z . We prove convergence and validate our assumptions using various real-world multi-source industrial datasets on a streaming consensus clustering task. In contrast to conventional pipelines that accumulate noise over time, require costly O ( N 2 ) co-association matrices, or O ( N 3 ) eigendecompositions, SCENTS yields a linear memory consensus embedding that improves monotonically along the streaming horizon and produces high-quality partitions. Moreover, SCENTS is designed to be integrated with any fixed encoder, enabling the implementation of a lightweight streaming consensus adaptation layer. The resulting embeddings offer a compact, reusable representation that supports various downstream tasks beyond clustering, thereby providing a scalable and generalisable fusion layer for data analysis.