1 code implementation • 8 Jan 2024 • Vijay Ekambaram, Arindam Jati, Pankaj Dayama, Sumanta Mukherjee, Nam H. Nguyen, Wesley M. Gifford, Chandra Reddy, Jayant Kalagnanam
Large pre-trained models excel in zero/few-shot learning for language and vision tasks but face challenges in multivariate time series (TS) forecasting due to diverse data characteristics.
Ranked #3 on Time Series Forecasting on ETTh1 (96) Multivariate
no code implementations • 28 Nov 2022 • Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami
To address this test-validation mismatch, we propose a novel technique, H-Pro to drive HPO via test proxies by exploiting data hierarchies often associated with time series datasets.
no code implementations • 4 Nov 2021 • Kyongmin Yeo, Zan Li, Wesley M. Gifford
We present a deep learning model for data-driven simulations of random dynamical systems without a distributional assumption.
no code implementations • 24 Feb 2021 • Syed Yousaf Shah, Dhaval Patel, Long Vu, Xuan-Hong Dang, Bei Chen, Peter Kirchner, Horst Samulowitz, David Wood, Gregory Bramble, Wesley M. Gifford, Giridhar Ganapavarapu, Roman Vaculin, Petros Zerfos
We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset.