Long-term time-series forecasting (LTSF) has received an increasing attention for its significant challenges and real-world applications. However, the previous studies under-explore the hierarchical timestamp information in LTSF. This information is crucial, especially for LTSF as failing to incorporate it may result in missing the global perspective of time series and important long-term trending effects, such as weekly and seasonal patterns. Therefore, we propose an interpretable hierarchical model called VH-NBEATS, which advances the N-BEATS model by addressing the aforementioned problem. VH-NBEATS comprises two essential blocks: the hierarchical timestamp block and the harmonic seasonal block to capture multi-diluted and trending effects. To address the high variability of time series, VH-NBEATS involves a stochastic autoencoder which significantly improves the standard deterministic approach. The experimental results are evaluated on five real-world datasets, showing state-of-the-art results for LTSF. We also prove that the VH-NBEATS framework can be easily incorporated into other ones, such as PathTST, leading to enhanced performance.