Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

Probabilistic forecasting that quantifies the prediction uncertainties is crucial for decision-
making in power systems. As a prevalent nonparametric probabilistic forecasting approach …

Accurate uncertainty measurement is a key step in building robust and reliable machine
learning systems. Conformal prediction is a distribution-free uncertainty quantification …

Abstract We propose Multivariate Quantile Function Forecaster (MQF2), a global
probabilistic forecasting method constructed using a multivariate quantile function and …

In the field of behavior-related brain computation, it is necessary to align raw neural signals
against the drastic domain shift among them. A foundational framework within neuroscience …

Although black-box models can accurately predict outcomes such as weather patterns, they
often lack transparency, making it challenging to extract meaningful insights (such as which …

Probabilistic time series forecasting has played critical role in decision-making processes
due to its capability to quantify uncertainties. Deep forecasting models, however, could be …

A normalizing flow (NF) is a mapping that transforms a chosen probability distribution to a
normal distribution. Such flows are a common technique used for data generation and …

Time series has been left behind in the era of pre-training and transfer learning. While
research in the fields of natural language processing and computer vision are enjoying …

Shared mobility services require accurate demand models for effective service planning. On
the one hand, modeling the full probability distribution of demand is advantageous because …