JaGuard recasts GNSS jamming mitigation as dynamic graph regression and uses a Heterogeneous Graph ConvLSTM to estimate 2D positional deviation, achieving MAEs of 2.26-2.61 cm on mixed-power real datasets and remaining stable under severe jamming and data starvation.
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Arik, and Tomas Pfister
14 Pith papers cite this work, alongside 122 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.
A pretrained decoder-only patched transformer achieves near state-of-the-art zero-shot forecasting performance across diverse time series datasets and settings.
Optimized Ridge regression with series-specific preprocessing beats prior linear forecasters and exceeds Transformer, MLP, and CNN baselines on six of eight time-series benchmarks.
QuITE is a query-token embedding layer that aggregates irregular observations via self-attention to produce backbone-compatible latents without generating artificial values.
STAIR's three-stage training enables simple temporal models to match or exceed complex baselines on long-term forecasting benchmarks by combining shared learning, individual adaptation, and residual cross-variable modeling.
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
CLOUDADV combines zero-shot forecasting with LLM-generated recommendations for cloud instance sizing, reporting 52.9% simulated monthly cost savings in a seven-VM Azure case study.
The self-adaptive scale-handling (AS) module with scale calibrating and scaling selection learns per-input scale factors to improve forecasting on scale-heterogeneous time series.
CHAM-net is a contrastive hierarchical adaptive meta-network that conditions predictions on historical site data to outperform baselines on methane flux tasks with nRMSE down to 0.43.
GenHAR generalizes cross-domain human activity recognition by 9.97% accuracy and 6.4x lower FLOPs via tokenized sensor data, frequency channel correlations, selective masking, and efficient attention, with deployment detecting 2.15 billion activities.
A single-layer architecture called FlowMixer uses constrained matrix operations and a semi-group property to enable depth-agnostic, interpretable spatiotemporal forecasting with direct eigenmode extraction.
Affine mapping dominates LTSF benchmarks by learning similar input-to-output transition matrices, captures periodic signals well but struggles with non-periodic or cross-channel varying periods; reversible normalization converts trends to periodic-like patterns.
Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.
citing papers explorer
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JaGuard: Position Error Correction of GNSS Jamming with Deep Temporal Graphs
JaGuard recasts GNSS jamming mitigation as dynamic graph regression and uses a Heterogeneous Graph ConvLSTM to estimate 2D positional deviation, achieving MAEs of 2.26-2.61 cm on mixed-power real datasets and remaining stable under severe jamming and data starvation.
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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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A decoder-only foundation model for time-series forecasting
A pretrained decoder-only patched transformer achieves near state-of-the-art zero-shot forecasting performance across diverse time series datasets and settings.
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How Good Can Linear Models Be for Time-Series Forecasting?
Optimized Ridge regression with series-specific preprocessing beats prior linear forecasters and exceeds Transformer, MLP, and CNN baselines on six of eight time-series benchmarks.
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QuITE: Query-Based Irregular Time Series Embedding
QuITE is a query-token embedding layer that aggregates irregular observations via self-attention to produce backbone-compatible latents without generating artificial values.
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Three-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecasting
STAIR's three-stage training enables simple temporal models to match or exceed complex baselines on long-term forecasting benchmarks by combining shared learning, individual adaptation, and residual cross-variable modeling.
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Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
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CLOUDADV: Decision-Aligned Instance Sizing with Zero-Shot Foundation Models under Drift
CLOUDADV combines zero-shot forecasting with LLM-generated recommendations for cloud instance sizing, reporting 52.9% simulated monthly cost savings in a seven-VM Azure case study.
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Self-Adaptive Scale Handling for Forecasting Time Series with Scale Heterogeneity
The self-adaptive scale-handling (AS) module with scale calibrating and scaling selection learns per-input scale factors to improve forecasting on scale-heterogeneous time series.
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CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction
CHAM-net is a contrastive hierarchical adaptive meta-network that conditions predictions on historical site data to outperform baselines on methane flux tasks with nRMSE down to 0.43.
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GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery
GenHAR generalizes cross-domain human activity recognition by 9.97% accuracy and 6.4x lower FLOPs via tokenized sensor data, frequency channel correlations, selective masking, and efficient attention, with deployment detecting 2.15 billion activities.
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FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting
A single-layer architecture called FlowMixer uses constrained matrix operations and a semi-group property to enable depth-agnostic, interpretable spatiotemporal forecasting with direct eigenmode extraction.
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Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping
Affine mapping dominates LTSF benchmarks by learning similar input-to-output transition matrices, captures periodic signals well but struggles with non-periodic or cross-channel varying periods; reversible normalization converts trends to periodic-like patterns.
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Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting
Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.