How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs
represent facts about entities and their relations, where each fact is associated with a …

This work proposes a novel approach for multiple time series forecasting. At first, multi-way
delay embedding transform (MDT) is employed to represent time series as low-rank block …

Activity logs collected from wearable devices (eg Apple Watch, Fitbit, etc.) are a promising
source of data to facilitate a wide range of applications such as personalized exercise …

Time series forecasting is a key ingredient in the automation and optimization of business
processes: in retail, deciding which products to order and where to store them depends on …

Soil respiration time series data exhibit obvious non-stationarity, with diurnal fluctuations
and susceptibility to various environmental factors. While traditional autoregressive models …

Travel time prediction of a given trajectory plays an indispensable role in intelligent
transportation systems. Although many prior researches have struggled for accurate …

Data-driven aero-engine remaining useful life (RUL) estimation is a key technology to
monitor engine's degradation. However, due to the difficulties of extracting the time …

Given a sparse time-evolving tensor, how can we effectively factorize it to accurately
discover latent patterns? Tensor decomposition has been extensively utilized for analyzing …

Tucker decomposition has been widely used in a variety of applications to obtain latent
factors of tensor data. In these applications, a common need is to compute Tucker …

Time series prediction has been a long-standing research topic and an essential application
in many domains. Modern time series collected from sensor networks (eg, energy …