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Shubham Kapoor

3 accepted papers

2025

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables

Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari, Lorenzo Stella, Huibin Shen +7
AISTATS 2025poster

Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, covariates may indicate promotions or peak dates such as holiday seasons that heavily influence demand forecasts. Recent a…

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2023

Coherent Probabilistic Forecasting of Temporal Hierarchies

Syama Sundar Rangapuram, Shubham Kapoor, Rajbir Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang +1
AISTATS 2023poster

Forecasts at different time granularities are required in practice for addressing various business problems starting from short-term operational to medium-term tactical and to long-term strategic planning. These forecasting problems are usually treated independently by learning different ML models w…

Cited by 20Source ↗PDF ↗Scholar ↗
2022

PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series

Paul Jeha, Michael Bohlke-Schneider, Pedro Mercado, Shubham Kapoor, Rajbir Singh Nirwan, Valentin Flunkert +2
ICLR 2022poster

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 we present PSA-GAN, a generative adversarial network (GAN) that generates long time series samples of high quality using…

Cited by 63Source ↗PDF ↗code ↗Scholar ↗