Neuralized Multi-Wavelet Decomposition for Time Series Classification and Forecasting

arXiv:2609.29317 · cs.LG, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/Beihang-BIGSCity/mwcn

Terminology

• Multi-Wavelet Decomposition — 25× in this paper
• Time Series Forecasting — 19× in this paper · explained in 3 episodes
• Time Series Classification — 18× in this paper
• Transformer — 17× in this paper · explained in 2 episodes
• Contrastive Learning — 13× in this paper · explained in 5 episodes
• Regularization — 13× in this paper · explained in 3 episodes
• Representation Learning — 9× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Adaptability — 7× in this paper · explained in 1 episode
• Frequency Contrastive Learning — 6× in this paper
• Frequency Representation Pre-training — 5× in this paper
• Wavelet Transform — 5× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Discrete Fourier Transform — 3× in this paper
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Activation Function — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Probabilistic Forecasting — 2× in this paper · explained in 3 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Time-Frequency Boosting Classification — 2× in this paper
• 1D CNN — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Discrete Multi-Wavelet Transform — 1× in this paper
• Discrete Wavelet Transform — 1× in this paper
• Electricity Transformer Temperature — 1× in this paper
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mean Squared Error — 1× in this paper
• Pattern Analysis — 1× in this paper
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• TSMixer: An All-MLP Architecture for Time Series Forecasting

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