Multi-resolution Time-Series Transformer for Long-term Forecasting

arXiv:2311.04147v2 Announce Type: replace
Abstract: 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), …

abstract architectures arxiv cs.lg forecasting learn long-term patterns performance resolution series temporal tokens transformer transformers type