15 citations · 40 across the 11 of their papers we have counts for
3 papers · 1 filter
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…
Investigating Compositional Reasoning in Time Series Foundation Models
Willa Potosnak, Cristian Challu, Mononito Goswami +4
Large pre-trained time series foundation models (TSFMs) have demonstrated promising zero-shot performance across a wide range of domains. However, a question remains: Do TSFMs succ…