  The performance of transformers for time-series forecasting has improved
significantly. Recent architectures learn complex temporal patterns by
segmenting a time-series into patches and using the patches as tokens. The
patch size controls the ability of transformers to learn the temporal patterns
at different frequencies: shorter patches are effective for learning localized,
high-frequency patterns, whereas mining long-term seasonalities and trends
requires longer patches. Inspired by this observation, we propose a novel
framework, Multi-resolution Time-Series Transformer (MTST), which consists of a
multi-branch architecture for simultaneous modeling of diverse temporal
patterns at different resolutions. In contrast to many existing time-series
transformers, we employ relative positional encoding, which is better suited
for extracting periodic components at different scales. Extensive experiments
on several real-world datasets demonstrate the effectiveness of MTST in
comparison to state-of-the-art forecasting techniques.
