SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
Transformers in time series: a survey
9 Pith papers cite this work. Polarity classification is still indexing.
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PI-DLinear integrates derived thermal ODEs into DLinear to forecast AI data center power more accurately than SOTA models while respecting physical constraints under throttling and transients.
DSTAN-Med uses separate sensor-wise and time-wise attention plus a zero-parameter physiological filter to detect falsified vital signs, reporting 7.4-8.3 percentage point sensitivity gains over Transformer baselines on three public datasets.
TTCD uses a non-stationary feature learner and reconstruction-guided distillation inside a transformer to infer contemporaneous and lagged causal graphs from non-stationary time series without strong noise assumptions.
RoMAE applies rotary positional embeddings to masked autoencoders to enable representation learning and interpolation on continuous positional data across irregular time-series, images, and audio without modality-specific modifications.
Exformer adds an extreme-aware attention component to standard Transformers and reports better 3-day streamflow forecasts than baselines on four hydrologic datasets.
TA-SparseMG extends SparseTSF with trend-aware reversible instance normalization, scale-adaptive gated denoising, and multiscale gated-attention MLP modules to achieve superior performance on long-term time series forecasting benchmarks.
Hybrid CNN-LSTM intrusion detection system achieves 98.2% precision on NSL-KDD and real-time throughput of 27,800 flows/s for smart grid cybersecurity.
A review chapter covering basic time series concepts, classical models like ARIMA, and ML approaches including neural networks and transformers.
citing papers explorer
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SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
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A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers
PI-DLinear integrates derived thermal ODEs into DLinear to forecast AI data center power more accurately than SOTA models while respecting physical constraints under throttling and transients.
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DSTAN-Med: Dual-Channel Spatiotemporal Attention with Physiological Plausibility Filtering for False Data Injection Attack Detection in IoT-Based Medical Devices
DSTAN-Med uses separate sensor-wise and time-wise attention plus a zero-parameter physiological filter to detect falsified vital signs, reporting 7.4-8.3 percentage point sensitivity gains over Transformer baselines on three public datasets.
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TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
TTCD uses a non-stationary feature learner and reconstruction-guided distillation inside a transformer to infer contemporaneous and lagged causal graphs from non-stationary time series without strong noise assumptions.
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Rotary Masked Autoencoders are Versatile Learners
RoMAE applies rotary positional embeddings to masked autoencoders to enable representation learning and interpolation on continuous positional data across irregular time-series, images, and audio without modality-specific modifications.
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Extreme Adaptive Transformer for Time Series Forecasting
Exformer adds an extreme-aware attention component to standard Transformers and reports better 3-day streamflow forecasts than baselines on four hydrologic datasets.
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TA-SparseMG: Trend-Aware Sparse Forecasting via Multi-Scale Gating for Long-Term Time Series
TA-SparseMG extends SparseTSF with trend-aware reversible instance normalization, scale-adaptive gated denoising, and multiscale gated-attention MLP modules to achieve superior performance on long-term time series forecasting benchmarks.
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A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids
Hybrid CNN-LSTM intrusion detection system achieves 98.2% precision on NSL-KDD and real-time throughput of 27,800 flows/s for smart grid cybersecurity.
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Time Series Analysis in Machine Learning
A review chapter covering basic time series concepts, classical models like ARIMA, and ML approaches including neural networks and transformers.