Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …

Missing data in time series is a pervasive problem that puts obstacles in the way of
advanced analysis. A popular solution is imputation, where the fundamental challenge is to …

Many real-world applications require the prediction of long sequence time-series, such as
electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a …

We present a novel framework for multivariate time series representation learning based on
the transformer encoder architecture. The framework includes an unsupervised pre-training …

Missing data imputation is a technique to deal with incomplete datasets. Since many models
and algorithms cannot be applied to data containing missing values, a pre-processing step …

Multivariate time series forecasting has been widely used in various practical scenarios.
Recently, Transformer-based models have shown significant potential in forecasting tasks …

Coded aperture snapshot spectral imaging (CASSI) is an effective tool to capture real-world
3D hyperspectral images. While a number of existing work has been conducted for …

The ubiquitous missing values cause the multivariate time series data to be partially
observed, destroying the integrity of time series and hindering the effective time series data …

The missing values, widely existed in multivariate time series data, hinder the effective data
analysis. Existing time series imputation methods do not make full use of the label …

Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow
modeling, and climate forecasting. However, the originally collected spatiotemporal data in …