aiqbench analysis
This paper introduces SCPaT, a Transformer-based framework that improves multivariate time series forecasting by organizing temporal data into semantic blocks rather than relying on rigid fixed partitions. By modeling directed dependencies among these units and routing them to specialized experts, the method addresses limitations in existing patch-based approaches regarding boundary preservation and redundant representations. The approach demonstrates enhanced performance across diverse real-world datasets, offering a more robust architecture for handling heterogeneous patterns in sequential data.
Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning
arXiv:2608.19966v2 Announce Type: replace Abstract: Multivariate time series forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based forecasting methods generally fall into three categories: fixed partitioning, multi-scale partitioning, and extendable partitioning. Fixed partitioning often breaks meaningful temporal boundaries, multi-scale partitioning may introduce redundant representations across scales, and extendable partitioning improves flexibility but still lacks an explicit mechanism for organizing semantic structure and modeling interactions among heterogeneous temporal patterns. To address these limitations, we propose SCPaT, a Transformer based framework built on semantic structured partitioning. SCPaT first decomposes input sequences into semantically consistent units through adaptive semantic unit generation, then constructs a dynamic semantic graph to model directed dependencies among these units and organize them into higher order semantic blocks. Based on these structured representations, an importance aware routing mechanism adaptively dispatches different semantic blocks to different experts for customized modeling. Extensive experiments on 12 real world datasets demonstrate the effectiveness of SCPaT.
Abstract:Multivariate time series forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based forecasting methods generally fall into three categories: fixed partitioning, multi-scale partitioning, and extendable partitioning. Fixed partitioning often breaks meaningful temporal boundaries, multi-scale partitioning may introduce redundant representations across scales, and extendable partitioning improves flexibility but still lacks an explicit mechanism for organizing semantic structure and modeling interactions among heterogeneous temporal patterns. To address these limitations, we propose SCPaT, a Transformer based framework built on semantic structured partitioning. SCPaT first decomposes input sequences into semantically consistent units through adaptive semantic unit generation, then constructs a dynamic semantic graph to model directed dependencies among these units and organize them into higher order semantic blocks. Based on these structured representations, an importance aware routing mechanism adaptively dispatches different semantic blocks to different experts for customized modeling. Extensive experiments on 12 real world datasets demonstrate the effectiveness of SCPaT.
Artificial Intelligence (cs.AI)
arXiv:2608.19966 [cs.AI]
(or arXiv:2608.19966v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.19966 Focus to learn more arXiv-issued DOI via DataCite
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