9 citations · 10 across the 3 of their papers we have counts for
4 papers
SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data
Cristian Challu, Peihong Jiang, Ying Nian Wu +1
In this work, we tackle two widespread challenges in real applications for time-series forecasting that have been largely understudied: distribution shifts and missing data. We pro…
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
Cristian Challu, Peihong Jiang, Ying Nian Wu +1
Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets.…
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
Cristian Challu, Kin G. Olivares, Gus Welter +1
Neural forecasting has shown significant improvements in the accuracy of large-scale systems, yet predicting extremely long horizons remains a challenging task. Two common problems…
Double Adaptive Stochastic Gradient Optimization
Kin Gutierrez, Jin Li, Cristian Challu +1
Adaptive moment methods have been remarkably successful in deep learning optimization, particularly in the presence of noisy and/or sparse gradients. We further the advantages of a…