Spatio-Temporal Forecasting
34 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
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Weather2K
Beijing Traffic
Latest papers with no code
Low-rank Adaptation for Spatio-Temporal Forecasting
Spatio-temporal forecasting is crucial in real-world dynamic systems, predicting future changes using historical data from diverse locations.
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach
From spatial perspective, we design lightweight-but-efficient prototypes as client-level semantic representations, based on which the server evaluates spatial similarity and yields client-customized global prototypes for the supplemented inter-client contrastive task.
DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting
In this paper, we introduce for the first time the concept of spatio-temporal data dynamic sparse training and are committed to adaptively, dynamically filtering important sensor distributions.
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions.
A Gated MLP Architecture for Learning Topological Dependencies in Spatio-Temporal Graphs
The Cy2Mixer is composed of three blocks based on MLPs: A message-passing block for encapsulating spatial information, a cycle message-passing block for enriching topological information through cyclic subgraphs, and a temporal block for capturing temporal properties.
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS).
SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting
Despite the significant strides made by graph-based networks in spatio-temporal forecasting, there remain two pivotal factors closely related to forecasting performance that need further consideration: time delays in propagation dynamics and multi-scale high-dimensional interactions.
It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting
With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future.
RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting
We consider the specific use case of road traffic systems where incidents take the form of anomalous events, such as accidents or broken-down vehicles.
Deep learning for spatio-temporal forecasting -- application to solar energy
This thesis tackles the subject of spatio-temporal forecasting with deep learning.
