Retrieval-Augmented Forecasting with Tabular Time Series Data (2025)

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).

## introduction forecasting economic and financial indicators using tabular time-series data is a high-stakes challenge. consider a hedge fund analyst predicting next-quarter earnings for a portfolio of tech companies: they must synthesize historical financial statements (e.g., apple's quarterly revenue), macroeconomic trends (e.g., interest rates), and unstructured signals (e.g., news about supply chains). current approaches fall short in two key ways. first, traditional time-series models like arima @xcite or prophet (taylor and letham, 2018) ignore cross-series dependencies-for instance, they cannot leverage the fact that nvidia's gpu sales may lag tsmc's wafer production by 3 months. second, while modern deep learning methods (e.g., temporal fusion transformers @xcite ) handle multivariate inputs, they treat tables as static matrices, failing to retrieve and contextualize relevant historical patterns. for example, during the 2022 oil crisis, a model unaware of analogous 2008 price shock dynamics would miss critical risk signals. this gap is exacerbated in retrieval-augmented generation (rag) systems, which excel in textbased qa @xcite but struggle with structured data. financial tables demand schemaaware retrieval (e.g., matching "ebitda" across filings with differing column names) and temporal alignment (e.g., retrieving q3 2020 data when forecasting q3 2023). we propose retrieval-augmented forecasting (raf) for tabular time series, which: (1) dynamically retrieves semantically and temporally relevant table slices (e.g., past oil price surges when predicting energy stocks), and (2) fuses them with neural forecasts via a schema-guided attention mechanism. our work is grounded in real-world needs, from bloomberg terminal users querying correlated assets to central banks simulating policy impacts across historical regimes. ## experiments and results building on the methodological foundations established in section 3, we now evaluate raf's performance across diverse forecasting scenarios. the experiments are designed to validate each component of our architecture while assessing practical utility in real-world conditions. ## discussion our results demonstrate three key advances over existing methods in tabular forecasting. first, raf's dynamic schema handling solves a fundamental limitation in prior work @xcite ; 5 ), our learned hashing mechanism maintains accuracy by focusing on statistical patterns rather than surface-level labels. this is particularly valuable in real-world applications like financial reporting, where companies frequently modify their presentation formats while maintaining underlying accounting principles. second, the integration of retrieval with forecasting addresses the temporal myopia problem identified in @xcite . while most neural forecasters focus on recent history, raf's ability to identify and incorporate relevant distant events (e.g., linking 2022 market conditions to 2008 crisis patterns) provides a more comprehensive context for predictions. this explains the particularly strong performance during volatile periods ( finally, our end-to-end training approach overcomes the suboptimality of pipeline systems noted in @xcite . by jointly optimizing the retriever and forecaster, raf ensures that retrieved tables are specifically useful for the forecasting task, rather than simply being semantically similar. the ablation study ( ## conclusion raf establishes a new state-of-the-art in tabular forecasting through its schema-aware retrieval and temporal fusion approach. by unifying dynamic column hashing, context-aware attention, and endto-end training, the framework outperforms specialized alternatives in both accuracy and robustness. real-world validation confirms its practical value for financial and economic prediction tasks where schema evolution and regime shifts are common. future work will extend the architecture to probabilistic forecasting and multimodal (table+text) retrieval scenarios.