Time series forecasting is vital in industries like weather and transportation. However, Transformer models may face challenges capturing both variable and temporal correlations in multivariate forecasting, potentially hindering their understanding of complex data dynamics. To address this, we present VPformer, a model that leverages cross-variable and temporal correlations to enhance forecasting accuracy. VPformer utilizes variable embedding and self-attention to explore variable correlations and transform raw data into a feature-rich space data. It segments new data into three patch types using non-overlapping partitioning and applies channel-independent techniques, sequence embedding, and attention mechanisms to capture temporal correlations. A fusion strategy integrates features, providing a holistic view that captures both variable and temporal information. VPformer’s performance is validated on real-world datasets, demonstrating superior forecasting accuracy and computational efficiency.