88 citations · 121 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 32 cited
Unified Training of Universal Time Series Forecasting Transformers
Gerald Woo, Chenghao Liu, Akshat Kumar +3
Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large…
cs.LG2023★ 1 cited
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
Gerald Woo, Chenghao Liu, Akshat Kumar +1
Time series has been left behind in the era of pre-training and transfer learning. While research in the fields of natural language processing and computer vision are enjoying prog…
cs.LG2022★ 88 cited
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo +2
Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from c…