Multivariate Time series forecasting is a foundation for data-centric decision making in domains such as traffic, energy, and industrial IoT, where accurately capturing complex dependencies between variables and over time is crucial. Among existing modeling paradigms, multi-scale (MS) approaches have been extensively explored for their strong capability to uncover correlations in time series, and recent studies have begun to extend this idea to multi-dependency (MD) modeling. However, existing approaches that couple MS and MD often repetitively apply multi-scale mechanism across different dependency types or simply treat scales as independent branches, which leads to redundant representations and fails to realize true hierarchical interaction across scales. To address this, we propose PyraTSG, a hierarchical scale transition framework that unifies multi-scale and multi-dependency modeling for multivariate time series forecasting. PyraTSG progressively aggregates fine-grained representations into coarse-grained ones through learnable scale transitions, enabling distinct dependencies to be captured at different hierarchical levels, expanding feature propagation ranges while maintaining efficiency. Extensive experiments on multiple benchmark datasets demonstrate that PyraTSG consistently outperforms state-of-the-art baselines, validating the effectiveness of hierarchical coupling for long-term forecasting.