OneCast: Structured Decomposition and Modular Generation for Cross-Domain Time Series Forecasting

• URL: http://arxiv.org/abs/2510.24028v2
• Date: Mon, 03 Nov 2025 01:49:39 GMT
• Title: OneCast: Structured Decomposition and Modular Generation for Cross-Domain Time Series Forecasting
• Authors: Tingyue Pan, Mingyue Cheng, Shilong Zhang, Zhiding Liu, Xiaoyu Tao, Yucong Luo, Jintao Zhang, Qi Liu,
• Abstract summary: Cross-domain time series forecasting is a valuable task in various web applications.<n>OneCast is a structured and modular forecasting framework that decomposes time series into seasonal and trend components.<n>Experiments across eight domains demonstrate that OneCast mostly outperforms state-of-the-art baselines.
• Score: 21.91411439072952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain time series forecasting is a valuable task in various web applications. Despite its rapid advancement, achieving effective generalization across heterogeneous time series data remains a significant challenge. Existing methods have made progress by extending single-domain models, yet often fall short when facing domain-specific trend shifts and inconsistent periodic patterns. We argue that a key limitation lies in treating temporal series as undifferentiated sequence, without explicitly decoupling their inherent structural components. To address this, we propose OneCast, a structured and modular forecasting framework that decomposes time series into seasonal and trend components, each modeled through tailored generative pathways. Specifically, the seasonal component is captured by a lightweight projection module that reconstructs periodic patterns via interpretable basis functions. In parallel, the trend component is encoded into discrete tokens at segment level via a semantic-aware tokenizer, and subsequently inferred through a masked discrete diffusion mechanism. The outputs from both branches are combined to produce a final forecast that captures seasonal patterns while tracking domain-specific trends. Extensive experiments across eight domains demonstrate that OneCast mostly outperforms state-of-the-art baselines.

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