@inproceedings{li-2025-retrieval,
    title = "Retrieval-Augmented Forecasting with Tabular Time Series Data",
    author = "Li, Zichao",
    editor = "Chang, Shuaichen  and
      Hulsebos, Madelon  and
      Liu, Qian  and
      Chen, Wenhu  and
      Sun, Huan",
    booktitle = "Proceedings of the 4th Table Representation Learning Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.trl-1.16/",
    pages = "192--199",
    ISBN = "979-8-89176-268-8",
    abstract = "This paper presents Retrieval-Augmented Forecasting (RAF), a novel framework for tabular time-series prediction that dynamically retrieves and integrates relevant historical table slices. RAF addresses three key limitations of existing methods: 1) schema rigidity through dynamic hashing of column metadata, 2) temporal myopia via cross-attention with learned decay, and 3) pipeline sub-optimality via end-to-end retriever-forecaster co-training. Experiments across macroeconomic (FRED-MD), financial (Yahoo Finance), and development (WorldBank) benchmarks demonstrate RAF{'}s superiority over six baselines, reducing sMAPE by 19.1-26.5{\%} while maintaining robustness to schema changes (+3.2{\%} sMAPE increase vs. +6.7-12.7{\%} for alternatives). The architecture{'}s computational overhead (1.8 vs. 1.2 hours/epoch vs. TFT) is justified by significant accuracy gains in critical scenarios like market shocks (61.7{\%} vs. 55.1{\%} directional accuracy)."
}