5 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Hierarchical Neural Additive Models for Interpretable Demand Forecasts
Leif Feddersen, Catherine Cleophas
Demand forecasts are the crucial basis for numerous business decisions, ranging from inventory management to strategic facility planning. While machine learning (ML) approaches off…
cs.LG2024★ 5 cited
The impact of data set similarity and diversity on transfer learning success in time series forecasting
Claudia Ehrig, Benedikt Sonnleitner, Ursula Neumann +2
Pre-trained models have become pivotal in enhancing the efficiency and accuracy of time series forecasting on target data sets by leveraging transfer learning. While benchmarks val…