Retrieval-Augmented Forecasting with Tabular Time Series Data

Zichao Li

Keywords: tabular forecasting, retrieval-augmented models, schema evolution, time-series analysis, neural databases, representation learning, financial prediction

TL;DR: RAF introduces retrieval-augmented forecasting for tables, combining dynamic schema hashing with temporal cross-attention to outperform existing methods by 19-26% on economic/financial benchmarks while handling schema changes robustly.

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).

Submission Number: 23