1 citations · 2 across the 8 of their papers we have counts for
9 papers
Bootstrap-Conditioned Action Selection with Tabular Foundation Models
Devansh Gupta, Shiv Tavker, Dmitry Efimov +3
Contextual bandits offer a natural framework for sample-efficient personalization, but practical deployment remains difficult under sparse, biased interaction data, unreliable unce…
Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables
Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova +7
In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load;…
Zero-shot Forecasting by Simulation Alone
Boris N. Oreshkin, Mayank Jauhari, Ravi Kiran Selvam +10
Zero-shot time-series forecasting holds great promise, but is still in its infancy, hindered by limited and biased data corpora, leakage-prone evaluation, and privacy and licensing…
Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility
Willa Potosnak, Malcolm Wolff, Mengfei Cao +6
While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs). Even…
A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models
Kin G. Olivares, Malcolm Wolff, Tatiana Konstantinova +8
Cross-frequency transfer learning (CFTL) has emerged as a popular framework for curating large-scale time series datasets to pre-train foundation forecasting models (FFMs). Althoug…
Probabilistic Pretraining for Neural Regression
Boris N. Oreshkin, Shiv Tavker, Dmitry Efimov
Transfer learning for probabilistic regression remains underexplored. This work closes this gap by introducing NIAQUE, Neural Interpretable Any-Quantile Estimation, a new model des…
TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting
Zhiyuan Zhao, Sitan Yang, Kin G. Olivares +5
Multi-horizon time series forecasting has many practical applications such as demand forecasting. Accurate demand prediction is critical to help make buying and inventory decisions…
LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data
Hanyu Zhang, Chuck Arvin, Dmitry Efimov +5
Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to ob…
SPADE Split Peak Attention DEcomposition
Malcolm Wolff, Kin G. Olivares, Boris Oreshkin +8
Demand forecasting faces challenges induced by Peak Events (PEs) corresponding to special periods such as promotions and holidays. Peak events create significant spikes in demand f…