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 …
A comprehensive study of speed prediction in transportation system: From vehicle to traffic
In the intelligent transportation system (ITS), speed prediction plays a significant role in
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …
Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values
Short-term traffic forecasting based on deep learning methods, especially recurrent neural
networks (RNN), has received much attention in recent years. However, the potential of RNN …
networks (RNN), has received much attention in recent years. However, the potential of RNN …
Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data
In real-world intelligent transportation systems, the spatiotemporal traffic data collected from
sensors often exhibit missing or corrupted data, significantly hindering the development of …
sensors often exhibit missing or corrupted data, significantly hindering the development of …
NT-DPTC: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation
Missing traffic data imputation is an important step in the intelligent transportation systems.
Low rank approximation is an important method for the missing traffic data imputation …
Low rank approximation is an important method for the missing traffic data imputation …
ImputeFormer: Low rankness-induced transformers for generalizable spatiotemporal imputation
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the
modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank …
modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank …
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method
Short-term origin–destination (OD) flow prediction in urban rail transit (URT) plays a crucial
role in smart and real-time URT operation and management. Different from other short-term …
role in smart and real-time URT operation and management. Different from other short-term …
Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent
transportation systems. Recent research has employed graph neural networks (GNNs) for …
transportation systems. Recent research has employed graph neural networks (GNNs) for …
Low-rank autoregressive tensor completion for spatiotemporal traffic data imputation
Spatiotemporal traffic time series (eg, traffic volume/speed) collected from sensing systems
are often incomplete with considerable corruption and large amounts of missing values …
are often incomplete with considerable corruption and large amounts of missing values …
Messages are never propagated alone: Collaborative hypergraph neural network for time-series forecasting
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 …
applications, an area of growing interest in a multitude of fields such as stock price …