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        <title>Retrieval-Augmented Forecasting with Tabular Time Series Data</title>
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        <namePart type="given">Zichao</namePart>
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        <name type="personal">
            <namePart type="given">Qian</namePart>
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    <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).</abstract>
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%0 Conference Proceedings
%T Retrieval-Augmented Forecasting with Tabular Time Series Data
%A Li, Zichao
%Y Chang, Shuaichen
%Y Hulsebos, Madelon
%Y Liu, Qian
%Y Chen, Wenhu
%Y Sun, Huan
%S Proceedings of the 4th Table Representation Learning Workshop
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-268-8
%F li-2025-retrieval
%X 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).
%R 10.18653/v1/2025.trl-1.16
%U https://aclanthology.org/2025.trl-1.16/
%U https://doi.org/10.18653/v1/2025.trl-1.16
%P 192-199

Markdown (Informal)

[Retrieval-Augmented Forecasting with Tabular Time Series Data](https://aclanthology.org/2025.trl-1.16/) (Li, TRL 2025)

• Retrieval-Augmented Forecasting with Tabular Time Series Data (Li, TRL 2025)

ACL

• Zichao Li. 2025. Retrieval-Augmented Forecasting with Tabular Time Series Data. In Proceedings of the 4th Table Representation Learning Workshop, pages 192–199, Vienna, Austria. Association for Computational Linguistics.