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Unitime: A language-empowered unified model for cross-domain time series forecasting
Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In
contrast to conventional methods that involve creating dedicated models for specific time …
contrast to conventional methods that involve creating dedicated models for specific time …
Heterogeneity-informed meta-parameter learning for spatiotemporal time series forecasting
Spatiotemporal time series forecasting plays a key role in a wide range of real-world
applications. While significant progress has been made in this area, fully capturing and …
applications. While significant progress has been made in this area, fully capturing and …
Multi-modality spatio-temporal forecasting via self-supervised learning
Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by
incorporating multiple modalities, which is prevalent in monitoring systems, encompassing …
incorporating multiple modalities, which is prevalent in monitoring systems, encompassing …
[HTML][HTML] Machine learning for human mobility during disasters: A systematic literature review
Understanding and predicting human mobility during disasters is crucial for effective disaster
management. Knowledge about population locations can greatly enhance rescue missions …
management. Knowledge about population locations can greatly enhance rescue missions …
Memda: forecasting urban time series with memory-based drift adaptation
Urban time series data forecasting featuring significant contributions to sustainable
development is widely studied as an essential task of the smart city. However, with the …
development is widely studied as an essential task of the smart city. However, with the …
Physics-informed neural ode for post-disaster mobility recovery
Urban mobility undergoes a profound decline in the aftermath of a disaster, subsequently
exhibiting a complex recovery trajectory. Effectively capturing and predicting this dynamic …
exhibiting a complex recovery trajectory. Effectively capturing and predicting this dynamic …
Forecasting lifespan of crowded events with acoustic synthesis-inspired segmental long short-term memory
Forecasting crowd congestion is crucial for ensuring comfortable mobility and public safety.
Existing methods forecast crowding by capturing the increase in planned visits, which …
Existing methods forecast crowding by capturing the increase in planned visits, which …
Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook
Data mining in transportation networks (DMTNs) refers to using diverse types of spatio-
temporal data for various transportation tasks, including pattern analysis, traffic prediction …
temporal data for various transportation tasks, including pattern analysis, traffic prediction …
Learning gaussian mixture representations for tensor time series forecasting
Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-
dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems …
dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems …
Multi-frequency spatial-temporal graph neural network for short-term metro OD demand prediction during public health emergencies
Short-term metro OD demand prediction during public health emergencies is a crucial task
for the effective management and operation of metro systems. However, such emergencies …
for the effective management and operation of metro systems. However, such emergencies …