most citedTEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

36 citations · 36 across the 3 of their papers we have counts for

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cs.LG2023

COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning

Chuizheng Meng, Yihe Dong, Sercan Ö. Arık +2

Estimation of temporal counterfactual outcomes from observed history is crucial for decision-making in many domains such as healthcare and e-commerce, particularly when randomized…

cs.LG2023★ 36 cited

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Defu Cao, Furong Jia, Sercan O Arik +4

The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary high…