Spatio-Temporal Prediction Models for Environmental and Urban Applications

The field of spatio-temporal prediction is rapidly advancing, with a focus on developing innovative models for environmental and urban applications. Recent research has emphasized the importance of accurately predicting air quality, traffic emissions, and human mobility patterns.

One of the key trends in this area is the integration of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to capture complex spatio-temporal dependencies. These models have shown significant improvements in prediction accuracy and generalization capabilities.

Another notable direction is the development of scale-disentangled spatio-temporal modeling frameworks, which aim to decompose and fuse features at different scales to improve long-term prediction performance. These frameworks have demonstrated state-of-the-art performance in traffic emission forecasting and other applications.

Noteworthy papers in this area include:

  • STPFormer, which proposes a pattern-aware spatio-temporal transformer for traffic forecasting, achieving state-of-the-art performance on multiple real-world datasets.
  • STM3, which introduces a mixture of multiscale Mamba architecture for long-term spatio-temporal time-series prediction, demonstrating superior performance on real-world benchmarks.
  • GeoMAE, which presents a novel contrastive self-learning framework for spatio-temporal graph forecasting with missing values, outperforming models trained from scratch on real-world datasets.

Sources

Air Quality PM2.5 Index Prediction Model Based on CNN-LSTM

Scale-Disentangled spatiotemporal Modeling for Long-term Traffic Emission Forecasting

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction

Training Machine Learning Models on Human Spatio-temporal Mobility Data: An Experimental Study [Experiment Paper]

STPFormer: A State-of-the-Art Pattern-Aware Spatio-Temporal Transformer for Traffic Forecasting

Forecasting Smog Events Using ConvLSTM: A Spatio-Temporal Approach for Aerosol Index Prediction in South Asia

Load Forecasting on A Highly Sparse Electrical Load Dataset Using Gaussian Interpolation

GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values

A Cost-Effective Framework for Predicting Parking Availability Using Geospatial Data and Machine Learning

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