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Found 10 papers, 8 papers with code
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Furthermore, our framework incorporates a stacking fusion module to combine diverse information from three types of curriculum learning, resulting in a strong and thorough learning process.
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.
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, characterized by extensive input sequences, as opposed to the shorter spans typical of traditional approaches.
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather.
Ranked #1 on
Traffic Prediction
on PeMSD7(M)
(using extra training data)
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge.
Ranked #2 on
Traffic Prediction
on PeMSD7
Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation
Few-shot question answering (QA) aims at precisely discovering answers to a set of questions from context passages while only a few training samples are available.
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 involving multi-source spatio-temporal data (e. g., transportation demands and air pollutants).
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns.
Ranked #1 on
Time Series Forecasting
on Weather (96)
A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting
Therefore, there are two fundamental views which can be used to analyze MTS data, namely the spatial view and the temporal view.
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.
