Abstract: Multivariate time series forecasting plays an important role in various applications ranging from meteorology study, traffic management to economics planning. In the past decades, many efforts have been made toward accurate and reliable forecasting by exploring both temporal dynamics and spatial correlation. Especially, the development of Transformer-based methods has significantly enhanced long-term forecasting accuracy in very recent years. The existing forecasting methods often assume intact input data, however, in practice the time series data is often partially observed due to device malfunction or costly data acquisition, which can seriously impede the performance of the existing approaches. A naive employment of imputation methods unavoidably involves error accumulation and leads to suboptimal solutions. Motivated by this, we propose a Biased Temporal Convolution Graph Network that jointly captures the temporal dependencies and spatial structure. In particular, we inject bias into the two carefully developed modules---the Multi-Scale Instance PartialTCN and Biased GCN---to account for missing patterns. The experimental results show that our proposed model is able to achieve up to $11$\% improvements over the existing methods on five real-world benchmark datasets. Code is available at this repository: https://anonymous.4open.science/r/BiaTCGNet-1F80/.