DT² trains digital twins to preserve pairwise policy rankings from fitted Q-evaluation on offline data rather than minimizing one-step transition errors, improving policy ranking and reducing decision regret.
Timexer: Empowering transformers for time series fore- casting with exogenous variables
8 Pith papers cite this work, alongside 49 external citations. Polarity classification is still indexing.
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This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
WeldMamba achieves 74.63% mIoU for 500 ms lookahead segmentation of keyhole, wire, and molten pool using spatiotemporal state space modeling conditioned on welding signals and physics-based losses on a 43-sequence dataset.
PatchECG applies masked patch training and disordered attention to handle asynchronous and partially missing ECG signals from varied layouts, reaching average AUROC 0.835 on simulated conditions and 0.778 on real hospital images for atrial fibrillation.
Zeus proposes a multi-scale Transformer with point-wise tokenization and Multi-Objective Temporal Masking to enable tuning-free performance on forecasting, interpolation, and other time series tasks.
A two-stage SFT-plus-RL framework gives a 7B language model step-by-step time series reasoning and beats or matches specialized forecasters on most of nine datasets.
Ister is a linear-complexity transformer using Dot-attention and inverted seasonal-trend decomposition for multivariate time series forecasting that reports state-of-the-art benchmark performance.
A two-stage residual-aware framework adds a meta-corrector after a base transformer to model structured errors and reports state-of-the-art results on eight time-series benchmarks.
citing papers explorer
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$\text{DT}^2$: Decision-Targeted Digital Twins
DT² trains digital twins to preserve pairwise policy rankings from fitted Q-evaluation on offline data rather than minimizing one-step transition errors, improving policy ranking and reducing decision regret.
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Deep Time Series Models: A Comprehensive Survey and Benchmark
This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
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Physics-Guided Spatiotemporal State Space Modeling for Lookahead Molten Pool Segmentation in Laser Wire-Feed Welding
WeldMamba achieves 74.63% mIoU for 500 ms lookahead segmentation of keyhole, wire, and molten pool using spatiotemporal state space modeling conditioned on welding signals and physics-based losses on a 43-sequence dataset.
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Masked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images
PatchECG applies masked patch training and disordered attention to handle asynchronous and partially missing ECG signals from varied layouts, reaching average AUROC 0.835 on simulated conditions and 0.778 on real hospital images for atrial fibrillation.
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Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis
Zeus proposes a multi-scale Transformer with point-wise tokenization and Multi-Objective Temporal Masking to enable tuning-free performance on forecasting, interpolation, and other time series tasks.
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Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
A two-stage SFT-plus-RL framework gives a 7B language model step-by-step time series reasoning and beats or matches specialized forecasters on most of nine datasets.
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Ister: Linear Transformer for Efficient Multivariate Time Series Forecasting
Ister is a linear-complexity transformer using Dot-attention and inverted seasonal-trend decomposition for multivariate time series forecasting that reports state-of-the-art benchmark performance.
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One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
A two-stage residual-aware framework adds a meta-corrector after a base transformer to model structured errors and reports state-of-the-art results on eight time-series benchmarks.