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Deep time series models: A comprehensive survey and benchmark
Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …
are ubiquitous in real-world applications. Different from other modalities, time series present …
Laplacian convolutional representation for traffic time series imputation
Spatiotemporal traffic data imputation is of great significance in intelligent transportation
systems and data-driven decision-making processes. To perform efficient learning and …
systems and data-driven decision-making processes. To perform efficient learning and …
Self-supervised generative adversarial learning with conditional cyclical constraints towards missing traffic data imputation
Accurate traffic data imputation aims to fill in missing traffic values with observations as much
as possible, which has long been a challenging task that affects its exploitation and …
as possible, which has long been a challenging task that affects its exploitation and …
Multi-stage deep residual collaboration learning framework for complex spatial–temporal traffic data imputation
Performing accurate and efficient traffic data repair has become an essential task before
proceeding with other applications of intelligent transportation systems. However, existing …
proceeding with other applications of intelligent transportation systems. However, existing …
Low-rank tensor completion with 3-D spatiotemporal transform for traffic data imputation
In recent years, the imputation of spatiotemporal traffic data has emerged as a critical area of
research within intelligent transportation systems. A commonly employed approach is low …
research within intelligent transportation systems. A commonly employed approach is low …
Discovering dynamic patterns from spatiotemporal data with time-varying low-rank autoregression
The problem of discovering interpretable dynamic patterns from spatiotemporal data is
studied in this paper. For that purpose, we develop a time-varying reduced-rank vector …
studied in this paper. For that purpose, we develop a time-varying reduced-rank vector …
Modeling dynamic traffic flow as visibility graphs: A network-scale prediction framework for lane-level traffic flow based on LPR data
Emerging applications in real-time traffic management put forward urgent requirements for
lane-level traffic flow prediction. Limited by extremely unstable traffic volumes and …
lane-level traffic flow prediction. Limited by extremely unstable traffic volumes and …
Fast and accurate parafac2 decomposition for time range queries on irregular tensors
How can we efficiently analyze a specific time range on an irregular tensor? PARAFAC2
decomposition is widely used when analyzing an irregular tensor which consists of several …
decomposition is widely used when analyzing an irregular tensor which consists of several …
Forecasting urban traffic states with sparse data using hankel temporal matrix factorization
Forecasting urban traffic states is crucial to transportation network monitoring and
management, playing an important role in the decision-making process. Despite the …
management, playing an important role in the decision-making process. Despite the …
High-dimensional fault tolerance testing of highly automated vehicles based on low-rank models
Ensuring fault tolerance of Highly Automated Vehicles (HAVs) is crucial for their safety due
to the presence of potentially severe faults. Hence, Fault Injection (FI) testing is conducted by …
to the presence of potentially severe faults. Hence, Fault Injection (FI) testing is conducted by …