[PDF][PDF] Outlier detection for time series with recurrent autoencoder ensembles.
We propose two solutions to outlier detection in time series based on recurrent autoencoder
ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent …
ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent …
Outlier detection for multidimensional time series using deep neural networks
Due to the continued digitization of industrial and societal processes, including the
deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered …
deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered …
AutoCTS: Automated correlated time series forecasting
Correlated time series (CTS) forecasting plays an essential role in many cyber-physical
systems, where multiple sensors emit time series that capture interconnected processes …
systems, where multiple sensors emit time series that capture interconnected processes …
Finding top-k shortest paths with diversity
The classical K Shortest Paths (KSP) problem, which identifies the k shortest paths in a
directed graph, plays an important role in many application domains, such as providing …
directed graph, plays an important role in many application domains, such as providing …
Predicting available parking slots on critical and regular services by exploiting a range of open data
Looking for available parking slots has become a serious issue in contemporary urban
mobility. The selection of suitable car parks could be influenced by multiple factors-eg, the …
mobility. The selection of suitable car parks could be influenced by multiple factors-eg, the …
Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks
Origin-destination (OD) matrices are used widely in transportation and logistics to record the
travel cost (eg, travel speed or greenhouse gas emission) between pairs of OD regions …
travel cost (eg, travel speed or greenhouse gas emission) between pairs of OD regions …
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles--Extended Version
With the sweeping digitalization of societal, medical, industrial, and scientific processes,
sensing technologies are being deployed that produce increasing volumes of time series …
sensing technologies are being deployed that produce increasing volumes of time series …
Stochastic weight completion for road networks using graph convolutional networks
Innovations in transportation, such as mobility-on-demand services and autonomous driving,
call for high-resolution routing that relies on an accurate representation of travel time …
call for high-resolution routing that relies on an accurate representation of travel time …
Correlated time series forecasting using multi-task deep neural networks
RG Cirstea, DV Micu, GM Muresan, C Guo… - Proceedings of the 27th …, 2018 - dl.acm.org
Cyber-physical systems often consist of entities that interact with each other over time.
Meanwhile, as part of the continued digitization of industrial processes, various sensor …
Meanwhile, as part of the continued digitization of industrial processes, various sensor …
Learning to route with sparse trajectory sets
Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route,
a comprehensive trajectory-based routing solution. Specifically, we first construct a graph …
a comprehensive trajectory-based routing solution. Specifically, we first construct a graph …