The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

Among various time series (TS) forecasting methods, ensemble forecast is extensively
acknowledged as a promising ensemble approach achieving great success in research and …

A variety of real-world applications rely on far future information to make decisions, thus
calling for efficient and accurate long sequence multivariate time series forecasting. While …

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic
forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …

Traffic time series forecasting is challenging due to complex spatio-temporal dynamics-time
series from different locations often have distinct patterns; and for the same time series …

Correlated time series (CTS) forecasting plays an essential role in many cyber-physical
systems, where multiple sensors emit time series that capture interconnected processes …

This paper delves into the problem of correlated time-series forecasting in practical
applications, an area of growing interest in a multitude of fields such as stock price …

We propose variational quasi-recurrent autoencoders (VQRAEs) to enable robust and
efficient anomaly detection in time series in unsupervised settings. The proposed VQRAEs …

Correlated traffic data refers to a collection of time series recorded simultaneously in
different regions throughout the same transportation network route. Due to the presence of …

Time series data occurs widely, and outlier detection is a fundamental problem in data
mining, which has numerous applications. Existing autoencoder-based approaches deliver …