Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Time series forecasting involves justifying assertions scientifically regarding potential states
or predicting future trends of an event based on historical data recorded at various time …

Time series forecasting has been a widely explored task of great importance in many
applications. However, it is common that real-world time series data are recorded in a short …

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 …

Online real-time traffic flow prediction typically offers better real-time performance than
offline prediction. However, existing studies rarely discussed online real-time traffic flow …

Probabilistic time series forecasting aims at estimating future probabilistic distributions
based on given time series observations. It is a widespread challenge in various tasks, such …

Accurately predicting traffic flow is a crucial upstream technique in intelligent transportation
systems for future travel plans, the efficiency of urban transport, and the regulation of …

Traffic flow usually contains complex nonlinear patterns. Deep learning can model nonlinear
fluctuations through iterative updates of trainable parameters. It generally requires a large …

Multivariate time series forecasting has been drawing increasing attention due to its
prevalent applications. It has been commonly assumed that leveraging latent dependencies …

In recent years, deep transfer learning techniques have been successfully applied to solve
RUL prediction across different working conditions. However, for RUL prediction across …