Existing Transformer-based time series forecasting models generally suffer from inadequate adaptive multi-scale capability and a decline in accuracy for long-term predictions. This paper proposes SAMSformer, a time series forecasting framework based on an adaptive multi-scale parallel Transformer for long-term forecasting. First, the model identifies the time series data characteristics through a sequence decomposition module. It then uses a dynamic selector to adaptively choose the time scales based on the data features and assigns weights to different scales. The multi-branch parallel Transformer structure models time segments of different scales in parallel. Each branch utilizes Local-Global Attention mechanism for parallel computing of local and global attention at different segment granularities. Local attention captures the local dependencies within each time segment, while global attention uses a multi-scale segment attention mechanism to extract global dependencies across time segments. Finally, to address the issue of accuracy decay in long-term predictions, the Time-Decaying Robust Loss function is introduced. Experiments on benchmark datasets from finance, transportation, electricity, and other domains demonstrate that the proposed method outperforms existing baseline models, making it a viable choice as a foundational architecture for time series forecasting.