OneCast: Structured Decomposition and Modular Generation for Cross-Domain Time Series Forecasting
This addresses the problem of cross-domain time series forecasting for web applications, offering an incremental improvement by explicitly decoupling structural components.
The paper tackled the challenge of generalizing across heterogeneous time series data by proposing OneCast, a framework that decomposes time series into seasonal and trend components for modular generation, and it demonstrated performance improvements over state-of-the-art baselines in experiments across eight domains.
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.