102 citations · 116 across the 5 of their papers we have counts for
5 papers
Fully Automated Correlated Time Series Forecasting in Minutes
Xinle Wu, Xingjian Wu, Dalin Zhang +4
Societal and industrial infrastructures and systems increasingly leverage sensors that emit correlated time series. Forecasting of future values of such time series based on record…
QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models -- Extended Version
David Campos, Bin Yang, Tung Kieu +3
We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine…
LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Version
David Campos, Miao Zhang, Bin Yang +3
Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision…
AutoPINN: When AutoML Meets Physics-Informed Neural Networks
Xinle Wu, Dalin Zhang, Miao Zhang +5
Physics-Informed Neural Networks (PINNs) have recently been proposed to solve scientific and engineering problems, where physical laws are introduced into neural networks as prior…
Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
Xinle Wu, Dalin Zhang, Miao Zhang +3
Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The ke…