Aiming at the problem of insufficient prediction accuracy of models caused by complex nonlinear dependencies and temporal shifts between channels in multivariate time series forecasting, this paper proposes a multivariate time series forecasting model based on dilated convolution and channel attention mechanism (Dilated Convolutional Time Series Transformer, DCTST). Firstly, an inverted embedding module independently encodes the entire historical sequence of each variable into high-dimensional tokens, preserving global temporal features. Secondly, a channel attention module is designed, combining a self-attention mechanism with a learnable routing strategy, and a dilated convolution module is introduced to expand the receptive field along the variable dimension to capture multi-scale local dependencies. Finally, cross-layer residual connections integrate the original input with deep-level features, enhancing model stability. Theoretical analysis and experiments show that compared to mainstream channel-independent models (e.g., PatchTST) and channel-dependent models (e.g., iTransformer), DCTST achieves a significant reduction in both Mean Squared Error (MSE) and Mean Absolute Error (MAE) on multiple real-world and synthetic datasets. The experimental results indicate that DCTST can effectively model inter-channel dependencies, improving the prediction accuracy and robustness of multivariate time series, making it suitable for practical engineering scenarios such as smart grids and traffic flow forecasting.