Crossformer implements a Transformer architecture with dimension-segment-wise embedding and two-stage attention mechanisms to capture cross-dimension dependencies in time series data. The model uses a hierarchical encoder-decoder structure for multi-scale forecasting. This is a research implementation (PyTorch) for multivariate time series forecasting tasks such as energy, weather, and traffic prediction.