  Long-term time series forecasting (LTSF) has been widely applied in finance,
traffic prediction, and other domains. Recently, patch-based transformers have
emerged as a promising approach, segmenting data into sub-level patches that
serve as input tokens. However, existing methods mostly rely on predetermined
patch lengths, necessitating expert knowledge and posing challenges in
capturing diverse characteristics across various scales. Moreover, time series
data exhibit diverse variations and fluctuations across different temporal
scales, which traditional approaches struggle to model effectively. In this
paper, we propose a dynamic tokenizer with a dynamic sparse learning algorithm
to capture diverse receptive fields and sparse patterns of time series data. In
order to build hierarchical receptive fields, we develop a multi-scale
Transformer model, coupled with multi-scale sequence extraction, capable of
capturing multi-resolution features. Additionally, we introduce a group-aware
rotary position encoding technique to enhance intra- and inter-group position
awareness among representations across different temporal scales. Our proposed
model, named DRFormer, is evaluated on various real-world datasets, and
experimental results demonstrate its superiority compared to existing methods.
Our code is available at: https://github.com/ruixindingECNU/DRFormer.
