MacroLens is a point-in-time multi-signal benchmark dataset and seven tasks for evaluating contextual financial reasoning models under macroeconomic scenarios.
Beyond trend and periodic- ity: Guiding time series forecasting with textual cues
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Introduces the task of counterfactual time series forecasting with textual conditions plus a text-attribution mechanism that improves accuracy by distinguishing mutable from immutable factors.
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
GRAFT improves electric load forecasting accuracy by aligning multi-source daily texts with half-hour load series and using cross-attention fusion, outperforming baselines on a new Australian benchmark across hourly to monthly horizons.
citing papers explorer
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MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios
MacroLens is a point-in-time multi-signal benchmark dataset and seven tasks for evaluating contextual financial reasoning models under macroeconomic scenarios.
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What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
Introduces the task of counterfactual time series forecasting with textual conditions plus a text-attribution mechanism that improves accuracy by distinguishing mutable from immutable factors.
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Incident-Guided Spatiotemporal Traffic Forecasting
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
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GRAFT: Grid-Aware Load Forecasting with Multi-Source Textual Alignment and Fusion
GRAFT improves electric load forecasting accuracy by aligning multi-source daily texts with half-hour load series and using cross-attention fusion, outperforming baselines on a new Australian benchmark across hourly to monthly horizons.