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u/ResponseAggressive36

r/CanadaUniversities r/UofT
▲ 1 r/UofT+1 crossposts

Can someone provide the list of papers below (include your btc address if you do)

  1. [tsLLM-SG: Time Series Forecasting by Fine-Tuning LLMs with Dynamic Soft Prompts and Gated Frequency Transformation Adapters](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_32.pdf)

  2. [SMART: A Statistic-Driven Semantic Alignment and Routing Framework for Multivariate Long-Term Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38410-2\_31.pdf)

  3. [Dual-MoE: Decoupled Cross-Modal Calibration with Scenario-Aware Expert Routing for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_41.pdf)

  4. [TimeGFD: A Lightweight Client-Side Time-Series Forecasting Framework Based on Generative Federated Distillation](https://link.springer.com/content/pdf/10.1007/978-981-92-2497-5\_10.pdf)

  5. [PSformer: Parameter-Efficient Transformer with Segment Shared Attention for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-31335-5\_32.pdf)

  6. [SA-MoE: Spectral-Aware Mixture of Experts for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_10.pdf)

  7. [PCR-Patch: Prompt-Calibrate-Refine Architecture for Short-Term Electricity Load Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_30.pdf)

  8. [TFMoE: Bridging Dual Domains for Long-Term Time Series Forecasting via Adaptive Time–Frequency Modeling](https://link.springer.com/content/pdf/10.1007/978-981-92-0375-8\_5.pdf)

  9. [DiSRet: Directional Structure-Aware Retentive Network for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0375-8\_6.pdf)

  10. [SFAT-Net: Non-stationary Time Series Forecasting with Multi-resolution Temporal Embedding and Adaptive Alignment](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_4.pdf)

  11. [SymMamba: A Symmetric Dual-Stream Framework for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-31930-2\_20.pdf)

  12. [Balancing Temporal and Inter-variable Dependencies in Multivariate Time-Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_17.pdf)

  13. [WMENet: A Multi-resolution Wavelet Mamba Enhancement Network for Long Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_2.pdf)

  14. [STMixMamba: A Lightweight Hybrid Model for User Trajectory Forecasting in Location-Based Services](https://link.springer.com/content/pdf/10.1007/978-981-92-5699-0\_16.pdf)

  15. [Dynamic Redundancy-Aware Variate Pruning: Striking the Efficiency-Accuracy Balance in Long-Term MTS Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3447-9\_36.pdf)

  16. [R2Net: Recurrent Residual Network Model for Time Series Prediction](https://link.springer.com/content/pdf/10.1007/978-3-032-37933-7\_6.pdf)

  17. [SCFT: A Dual-Domain Orthogonal Representation Framework for Efficient Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_22.pdf)

  18. [FW-Aug: Fourier-Wavelet Input Augmentation for Transformer-Based Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-2480-7\_11.pdf)

  19. [ClusterPatchTST: Uncertainty-Aware Causal Clustering for Heterogeneous Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_31.pdf)

  20. [S2DBM: Series-to-Series Brownian Bridge for Time Series Point Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_25.pdf)

  21. [Revisiting Long-Term Time Series Forecasting: An Empirical Perspective on Recent Approaches](https://link.springer.com/content/pdf/10.1007/978-3-032-21625-0\_43.pdf)

  22. [NuTS: Non-uniform Sampling with Distance-Dependent Adaptive Fusion for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0369-7\_28.pdf)

  23. [WaveFi-Patch: Strengthening Channel-Independent Long-Term Time Series Forecasting with Wavelet-Inspired Decomposition and Time-Aware Modulation](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_28.pdf)

  24. [A Temporal-Structure-Aware Enhancement Method for Industrial Process Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_29.pdf)

  25. [Adaptive Wavelet Decomposition-Based Dual-Path Transformer for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3394-6\_2.pdf)

  26. [Chaoslinear: A Time Series Forecasting Method Integrating Sequence Uncertainty with Deep Learning](https://link.springer.com/content/pdf/10.1007/978-981-92-3554-4\_21.pdf)

  27. [TFDF-Net: Time-Frequency Decomposition for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38401-0\_6.pdf)

  28. [Gaussian Process Conformal Prediction for Risk-Conditional Coverage Consistency](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_33.pdf)

  29. [TS-Unlearn: A Dual-Objective Unlearning Framework for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-1462-4\_27.pdf)

  30. [UniMixer: unified patch-wise and global inter-series dependency modeling for multivariate time series forecasting](https://link.springer.com/content/pdf/10.1007/978-981-95-4149-2\_30.pdf)

  31. [SparseCycleTransformer: Dual-Pathway Sparse Gating with Periodic Semantic Guidance for Efficient Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3447-9\_24.pdf)

  32. [Point-Patch Transformer for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0378-9\_25.pdf)

  33. [Periodicity-Aware Time Series Forecasting for Electric Load](https://link.springer.com/content/pdf/10.1007/978-981-95-9207-4\_4.pdf)

  34. [(model name missing in Scholar): A Multi-scale Multi-level Decomposition Transformer for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38404-1\_20.pdf)

  35. [Multi-domain Time Series Reconstruction with Frequency-Enhanced Transformers](https://link.springer.com/content/pdf/10.1007/978-981-92-2480-7\_33.pdf)

  36. [Frequency Energy Adaptive Normalization for Non-Stationary Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3391-5\_17.pdf)

  37. [DynaRoute: Dynamics-Conditioned Routing for Universal Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_48.pdf)

  38. [XML with XPath, XMLStarlet, and Python](https://link.springer.com/content/pdf/10.1007/979-8-8688-2077-9\_11.pdf) (off-topic)

  39. [GRFM: Graph-Aware Residual Forecasting via Periodic Decomposition and Residual Graph Modeling](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_9.pdf)

  40. [AMGNet: Dual-Domain Scale-Specific Graph Learning for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_42.pdf)

  41. [Multivariate Time Series Forecasting with Frequency-Domain Similarity Clustering and Hierarchical Feature Interaction](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_47.pdf)

  42. [AEFTS-Net: Period-Aware Spectral-Temporal Co-design for Efficient Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-2759-4\_22.pdf)

  43. [DPFT: A Framework for Decomposed Probabilistic Forecasting with Transformers](https://link.springer.com/content/pdf/10.1007/978-981-92-3450-9\_22.pdf)

  44. [DBHE: Dynamic Basis-Driven Hypernetwork Experts for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38404-1\_38.pdf)

  45. [DDCG: Dual-granularity Dual-domain Collaborative Graph Neural Networks for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-1300-9\_42.pdf)

  46. [SSM-Crossformer: Attention Mechanisms Based on Spatial-Spectral Masking in Crossformer for Multidimensional Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-08820-8\_7.pdf)

  47. [PAPT: Periodic-Aware Transformer with Polynomial Trend Fitting for General Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0375-8\_1.pdf)

  48. [MKAT: Mutual Kolmogorov-Arnold Transformer for Reliable Information Flow in Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-37676-3\_27.pdf)

  49. [PyraTSG: A Hierarchical Scale Transition Framework for Dependency Modeling in Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_16.pdf)

  50. [DPC-Net: A Decouple-Predict-Correct Framework for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0366-6\_40.pdf)

  51. [DMNet: Dual-Decomposition and Multi-frequency Differentiated Learning Architecture for Long-term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3438-7\_35.pdf)

  52. [EC-Flow: Entropy-Enhanced Contrastive Learning for Fixed-Point Constrained Time-Series Flow Matching](https://link.springer.com/content/pdf/10.1007/978-3-032-37676-3\_17.pdf)

  53. [MoE-TAPE: Mixture of Experts with Task-Adaptive Parameter-Efficient Module for Time Series Representation Learning](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_23.pdf)

  54. [NeuroCEA: Cross-Domain Embedding Augmentation with Contrastive Knowledge Distillation for Electrophysiological Time Series Representation](https://link.springer.com/content/pdf/10.1007/978-981-92-0369-7\_15.pdf)

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u/ResponseAggressive36 — 9 days ago