ROSE: Register Assisted General Time Series Forecasting with Decomposed Frequency Learning
<|reference_start|>ROSE: Register Assisted General Time Series Forecasting with Decomposed Frequency Learning: With the increasing collection of time series data from various domains, there arises a strong demand for general time series forecasting models pre-trained on a large number of time-series datasets to support a variety of downstream prediction tasks. Enabling general time series forecasting faces two challenges: how to obtain unified representations from multi-domian time series data, and how to capture domain-specific features from time series data across various domains for adaptive transfer in downstream tasks. To address these challenges, we propose a Register Assisted General Time Series Forecasting Model with Decomposed Frequency Learning (ROSE), a novel pre-trained model for time series forecasting. ROSE employs Decomposed Frequency Learning for the pre-training task, which decomposes coupled semantic and periodic information in time series with frequency-based masking and reconstruction to obtain unified representations across domains. We also equip ROSE with a Time Series Register, which learns to generate a register codebook to capture domain-specific representations during pre-training and enhances domain-adaptive transfer by selecting related register tokens on downstream tasks. After pre-training on large-scale time series data, ROSE achieves state-of-the-art forecasting performance on 8 real-world benchmarks. Remarkably, even in few-shot scenarios, it demonstrates competitive or superior performance compared to existing methods trained with full data.<|reference_end|>
@article{wang2024rose:,
    title={ROSE: Register Assisted General Time Series Forecasting with Decomposed
  Frequency Learning},
    author={Yihang Wang, Yuying Qiu, Peng Chen, Kai Zhao, Yang Shu, Zhongwen Rao,
  Lujia Pan, Bin Yang, Chenjuan Guo},
    journal={arXiv preprint arXiv:2405.17478},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17478},
    primaryClass={cs.LG stat.ML}
}