EpiCastBench supplies 40 curated multivariate epidemic datasets and evaluates 15 forecasting models under unified preprocessing, horizons, metrics, and significance tests.
arXiv preprint arXiv:2001.08317 , year=
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abstract
In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs Transformer-based machine learning models to forecast time series data. This approach works by leveraging self-attention mechanisms to learn complex patterns and dynamics from time series data. Moreover, it is a generic framework and can be applied to univariate and multivariate time series data, as well as time series embeddings. Using influenza-like illness (ILI) forecasting as a case study, we show that the forecasting results produced by our approach are favorably comparable to the state-of-the-art.
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A self-supervised multimodal alignment step plus equivariant GNN-based MARL yields over twofold sensing accuracy and 50% performance gains in decentralized V2I rate maximization.
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DualEngage fuses transformer-encoded student motion dynamics with 3D scene features via softmax-gated fusion to recognize group engagement in classroom videos, reporting 96.21% average accuracy on a university dataset.
Simple 4-layer CNN on raw candlestick charts achieves 0.892 AUC-ROC for cryptocurrency regime prediction and outperforms complex encodings and larger pretrained models.
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.
citing papers explorer
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EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting
EpiCastBench supplies 40 curated multivariate epidemic datasets and evaluates 15 forecasting models under unified preprocessing, horizons, metrics, and significance tests.
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Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems
A self-supervised multimodal alignment step plus equivariant GNN-based MARL yields over twofold sensing accuracy and 50% performance gains in decentralized V2I rate maximization.
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NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts
NEST improves long-term multivariate forecasting under dataset-level distribution shifts by clustering regimes in moment-entropy space and recomposing specialized variate-attention experts via a content-plus-geometry router.
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Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating
DualEngage fuses transformer-encoded student motion dynamics with 3D scene features via softmax-gated fusion to recognize group engagement in classroom videos, reporting 96.21% average accuracy on a university dataset.
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Visual Chart Representations for Cryptocurrency Regime Prediction: A Systematic Deep Learning Study
Simple 4-layer CNN on raw candlestick charts achieves 0.892 AUC-ROC for cryptocurrency regime prediction and outperforms complex encodings and larger pretrained models.
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Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.