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Found 5 papers, 1 papers with code
TsSHAP: Robust model agnostic feature-based explainability for time series forecasting
The explanations are in terms of the SHAP values obtained by applying the TreeSHAP algorithm on a surrogate model that learns a mapping between the interpretable feature space and the forecast of the black-box model.
Semi-supervised counterfactual explanations
Counterfactual explanations for machine learning models are used to find minimal interventions to the feature values such that the model changes the prediction to a different output or a target output.
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
To address this test-validation mismatch, we propose a novel technique, H-Pro to drive HPO via test proxies by exploiting data hierarchies often associated with time series datasets.
Attention based Multi-Modal New Product Sales Time-series Forecasting
Trend driven retail industries such as fashion, launch substantial new products every season.
Ranked #1 on
New Product Sales Forecasting
on VISUELLE2.0
(using extra training data)
New Product Sales Forecasting
Short-observation new product sales forecasting
+1
Semi-Supervised Method using Gaussian Random Fields for Boilerplate Removal in Web Browsers
Boilerplate removal refers to the problem of removing noisy content from a webpage such as ads and extracting relevant content that can be used by various services.
