18 citations · 37 across the 6 of their papers we have counts for
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cs.LG2023★ 1 cited
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift
Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor +3
The world is not static: This causes real-world time series to change over time through external, and potentially disruptive, events such as macroeconomic cycles or the COVID-19 pa…
cs.LG2021★ 14 cited
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
Jeha Paul, Bohlke-Schneider Michael, Mercado Pedro +5
Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper…