Applications in time series analysis

Class-incremental learning on multivariate time series via shape-aligned temporal distillation

Interpretable Multi-scale Neural Network for Granger Causality Discovery

Dynamic Vehicle Graph Interaction for Trajectory Prediction based on Video Signals

SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction

Learned Kalman Filtering in Latent Space with High-Dimensional Data

Robust Time Series Recovery and Classification Using Test-Time Noise Simulator Networks

OTW: Optimal Transport Warping for Time Series

Improving Electric Load Demand Forecasting with Anchor-based Forecasting Method

SADI: A SELF-ADAPTIVE DECOMPOSED INTERPRETABLE FRAMEWORK FOR ELECTRICITY LOAD FORECASTING UNDER EXTREME EVENTS

REDUCING THE COMPUTATIONAL COMPLEXITY OF LEARNING WITH RANDOM CONVOLUTIONAL FEATURES