ConTex learns a global intervention strategy via a decomposed temporal-conditional encoder architecture to generate consistent, sparse counterfactuals for time series models in a single forward pass.
N-hits: Neural hierarchical interpolation for time series forecasting
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
Pretrained TSFMs achieve top ranks on equity return tasks but show sparse, minimal improvements over random walk, serving as practical priors without reliable alpha generation.
A single-layer architecture called FlowMixer uses constrained matrix operations and a semi-group property to enable depth-agnostic, interpretable spatiotemporal forecasting with direct eigenmode extraction.
citing papers explorer
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ConTex: Reformulating Counterfactual Generation For Time Series Forecasting
ConTex learns a global intervention strategy via a decomposed temporal-conditional encoder architecture to generate consistent, sparse counterfactuals for time series models in a single forward pass.
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Pretrained Time-Series Foundation Models for Financial Return Forecasting
Pretrained TSFMs achieve top ranks on equity return tasks but show sparse, minimal improvements over random walk, serving as practical priors without reliable alpha generation.
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FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting
A single-layer architecture called FlowMixer uses constrained matrix operations and a semi-group property to enable depth-agnostic, interpretable spatiotemporal forecasting with direct eigenmode extraction.