Seasonal and Quantum-inspired Models for Neutron Monitor Time Series Forecasting

arXiv:2609.30281 · cs.LG, quant-ph · Submitted 2026-08-26 · Read on arXiv

cs.LG, quant-ph

Submitted: 2026-08-26

Updated: 2026-08-26

Terminology

• Robustness — 5× in this paper · explained in 5 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Temporal Convolutional Network — 4× in this paper
• Transformer — 4× in this paper · explained in 2 episodes
• Time Series Forecasting — 3× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Conformal Prediction — 2× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Hybrid Architectures — 2× in this paper · explained in 3 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Mean Absolute Error — 2× in this paper
• Root Mean Squared Error — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Long-Short Term Memory — 1× in this paper
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• Quantum Long Short-Term Memory — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• Variational Quantum Circuit — 1× in this paper
• Verification — 1× in this paper · explained in 2 episodes

Sources

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
• KAN: Kolmogorov-Arnold Networks
• QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
• Quantum Long Short-Term Memory
• A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
• Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators
• Auto-Encoding Variational Bayes
• WaveNet: A Generative Model for Raw Audio

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks