AOSNET is a Hilbert-guided framework that performs adaptive oscillatory-state alignment to a learnable prior for improved long-term forecasting under non-stationary conditions.
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Transformers in time series: A survey
19 Pith papers cite this work. Polarity classification is still indexing.
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HapticLDM is the first latent diffusion model that generates vibrotactile signals directly from text, using dynamic text curation and global denoising to improve realism and semantic alignment over autoregressive baselines.
FactoryBench reveals that frontier LLMs achieve under 50% on structured causal questions and under 18% on decision-making in industrial robotic telemetry.
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.
PatchTST uses subseries patching and channel-independent Transformers to deliver significantly better long-term multivariate time series forecasting and strong self-supervised transfer performance.
VESTA introduces dynamic tool creation for VLMs that outperforms static-tool and no-tool baselines on distribution fitting, time series, and astronomy tasks in the new DAWN benchmark.
Timer-S1 is a released 8.3B-parameter MoE time series model that achieves state-of-the-art MASE and CRPS scores on GIFT-Eval using serial scaling and Serial-Token Prediction.
Overlapping inference windows improve reconstruction-based time series anomaly detection by up to 28% relative gain across models on TSB-AD and UCR benchmarks and can alter rankings.
A momentum-corrected online stacking ensemble forecasts the new Kalimati Vegetable Price Index with RMSE 1.771, MAPE 0.68 percent, and R-squared 0.845 at the 90-day horizon.
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
A temporal memory-aware Transformer emulator for the Emanuel convective parameterization shows lower offline errors and 10-year stability in single-column model tests compared to memory-less MLP and LSTM baselines.
ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.
AR-KAN combines a pre-trained AR module with KAN to reduce redundancy while preserving temporal features, delivering lower probabilistic approximation error and stronger forecasting results on synthetic almost-periodic signals and real datasets.
PhysicsFormer applies a lightweight Transformer PINN with pseudo-sequential representations to convection, Burgers, lid-driven cavity, and inverse Navier-Stokes problems, reporting near-zero error in parameter identification and flow reconstruction from sparse noisy data.
Fourier-KAN-Mamba combines Fourier features, KAN nonlinearities, and Mamba state-space modeling with a gating mechanism and reports better anomaly detection performance than prior methods on the MSL, SMAP, and SWaT benchmarks.
Transformer-guided DRL cut training time steps to 25% of vanilla DRL while reaching 97.2% of optimal energy consumption for eVTOL takeoff versus 96.1%.
Proposes Adaptive Financial Transformer with regime-gated attention and a composite loss to predict stock returns while claiming to fix backtesting issues and reduce complexity by 15.2%.
A survey of positional encoding methods in transformer-based time series models that evaluates fixed, learnable, relative, and hybrid approaches on classification tasks and links effectiveness to data characteristics.
A federated learning framework lets distributed weather sensors train shared deep learning models for forecasting and anomaly detection while keeping raw data private.
citing papers explorer
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AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting
AR-KAN combines a pre-trained AR module with KAN to reduce redundancy while preserving temporal features, delivering lower probabilistic approximation error and stronger forecasting results on synthetic almost-periodic signals and real datasets.
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Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection
Fourier-KAN-Mamba combines Fourier features, KAN nonlinearities, and Mamba state-space modeling with a gating mechanism and reports better anomaly detection performance than prior methods on the MSL, SMAP, and SWaT benchmarks.
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Transformer-Guided Deep Reinforcement Learning for Optimal Takeoff Trajectory Design of an eVTOL Drone
Transformer-guided DRL cut training time steps to 25% of vanilla DRL while reaching 97.2% of optimal energy consumption for eVTOL takeoff versus 96.1%.
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Positional Encoding in Transformer-Based Time Series Models: A Survey
A survey of positional encoding methods in transformer-based time series models that evaluates fixed, learnable, relative, and hybrid approaches on classification tasks and links effectiveness to data characteristics.