Transformer-based models have greatly pushed the boundaries of time series
forecasting recently. Existing methods typically encode time series data into
$\textit{patches}$ using one or a fixed set of patch lengths. This, however,
could result in a lack of ability to capture the variety of intricate temporal
dependencies present in real-world multi-periodic time series. In this paper,
we propose MultiResFormer, which dynamically models temporal variations by
adaptively choosing optimal patch lengths. Concretely, at the beginning of each
layer, time series data is encoded into several parallel branches, each using a
detected periodicity, before going through the transformer encoder block. We
conduct extensive evaluations on long- and short-term forecasting datasets
comparing MultiResFormer with state-of-the-art baselines. MultiResFormer
outperforms patch-based Transformer baselines on long-term forecasting tasks
and also consistently outperforms CNN baselines by a large margin, while using
much fewer parameters than these baselines.

提出一种名为 MultiResFormer 的基于 Transformer 的模型，通过自适应选择最佳的 patch 长度来动态建模时间序列的变化，并在长期预测任务中优于基于 patch 的 Transformer，同时使用比 CNN 基线更少的参数。