Metadata Matters for Time Series: Informative Forecasting with Transformers
<|reference_start|>Metadata Matters for Time Series: Informative Forecasting with Transformers: Time series forecasting is prevalent in extensive real-world applications, such as financial analysis and energy planning. Previous studies primarily focus on time series modality, endeavoring to capture the intricate variations and dependencies inherent in time series. Beyond numerical time series data, we notice that metadata (e.g.~dataset and variate descriptions) also carries valuable information essential for forecasting, which can be used to identify the application scenario and provide more interpretable knowledge than digit sequences. Inspired by this observation, we propose a Metadata-informed Time Series Transformer (MetaTST), which incorporates multiple levels of context-specific metadata into Transformer forecasting models to enable informative time series forecasting. To tackle the unstructured nature of metadata, MetaTST formalizes them into natural languages by pre-designed templates and leverages large language models (LLMs) to encode these texts into metadata tokens as a supplement to classic series tokens, resulting in an informative embedding. Further, a Transformer encoder is employed to communicate series and metadata tokens, which can extend series representations by metadata information for more accurate forecasting. This design also allows the model to adaptively learn context-specific patterns across various scenarios, which is particularly effective in handling large-scale, diverse-scenario forecasting tasks. Experimentally, MetaTST achieves state-of-the-art compared to advanced time series models and LLM-based methods on widely acknowledged short- and long-term forecasting benchmarks, covering both single-dataset individual and multi-dataset joint training settings.<|reference_end|>
@article{dong2024metadata,
    title={Metadata Matters for Time Series: Informative Forecasting with
  Transformers},
    author={Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Li Zhang, Jianmin Wang,
  Mingsheng Long},
    journal={arXiv preprint arXiv:2410.03806},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03806},
    primaryClass={cs.LG cs.CL}
}