Meta-learning and Data Augmentation for Stress Testing Forecasting Models
Fuente:
arXiv
Guardado en:
| Autores principales: | Inácio, Ricardo, Cerqueira, Vitor, Barandas, Marília, Soares, Carlos |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Time Series Data Augmentation as an Imbalanced Learning Problem
por: Cerqueira, Vitor, et al.
Publicado: (2024)
por: Cerqueira, Vitor, et al.
Publicado: (2024)
Online Data Augmentation for Forecasting with Deep Learning
por: Cerqueira, Vitor, et al.
Publicado: (2024)
por: Cerqueira, Vitor, et al.
Publicado: (2024)
Simulating Biases for Interpretable Fairness in Offline and Online Classifiers
por: Inácio, Ricardo, et al.
Publicado: (2025)
por: Inácio, Ricardo, et al.
Publicado: (2025)
ModelRadar: Aspect-based Forecast Evaluation
por: Cerqueira, Vitor, et al.
Publicado: (2025)
por: Cerqueira, Vitor, et al.
Publicado: (2025)
Forecasting with Deep Learning: Beyond Average of Average of Average Performance
por: Cerqueira, Vitor, et al.
Publicado: (2024)
por: Cerqueira, Vitor, et al.
Publicado: (2024)
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
por: Leites, José, et al.
Publicado: (2024)
por: Leites, José, et al.
Publicado: (2024)
N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
por: Matos, Ricardo, et al.
Publicado: (2025)
por: Matos, Ricardo, et al.
Publicado: (2025)
Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction
por: Cerqueira, Vitor, et al.
Publicado: (2022)
por: Cerqueira, Vitor, et al.
Publicado: (2022)
L-GTA: Latent Generative Modeling for Time Series Augmentation
por: Roque, Luis, et al.
Publicado: (2025)
por: Roque, Luis, et al.
Publicado: (2025)
Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine
por: Roque, Luis, et al.
Publicado: (2024)
por: Roque, Luis, et al.
Publicado: (2024)
Grasynda: Graph-based Synthetic Time Series Generation
por: Amorim, Luis, et al.
Publicado: (2026)
por: Amorim, Luis, et al.
Publicado: (2026)
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training
por: Tao, Chen, et al.
Publicado: (2024)
por: Tao, Chen, et al.
Publicado: (2024)
Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction Forecasting
por: Jučas, Augustinas, et al.
Publicado: (2025)
por: Jučas, Augustinas, et al.
Publicado: (2025)
A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting
por: Saadallah, Ons, et al.
Publicado: (2026)
por: Saadallah, Ons, et al.
Publicado: (2026)
Stress-Testing ML Pipelines with Adversarial Data Corruption
por: Zhu, Jiongli, et al.
Publicado: (2025)
por: Zhu, Jiongli, et al.
Publicado: (2025)
Data Augmentation in Time Series Forecasting through Inverted Framework
por: Tan, Hongming, et al.
Publicado: (2025)
por: Tan, Hongming, et al.
Publicado: (2025)
DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
por: Suzuki, Masahiro, et al.
Publicado: (2026)
por: Suzuki, Masahiro, et al.
Publicado: (2026)
Overcoming Data Limitations in Internet Traffic Forecasting: LSTM Models with Transfer Learning and Wavelet Augmentation
por: Saha, Sajal, et al.
Publicado: (2024)
por: Saha, Sajal, et al.
Publicado: (2024)
Retrieval-Augmented Diffusion Models for Time Series Forecasting
por: Liu, Jingwei, et al.
Publicado: (2024)
por: Liu, Jingwei, et al.
Publicado: (2024)
Panda: Test-Time Adaptation with Negative Data Augmentation
por: Deng, Ruxi, et al.
Publicado: (2025)
por: Deng, Ruxi, et al.
Publicado: (2025)
Data Augmentation Policy Search for Long-Term Forecasting
por: Nochumsohn, Liran, et al.
Publicado: (2024)
por: Nochumsohn, Liran, et al.
Publicado: (2024)
MetaAug: Meta-Data Augmentation for Post-Training Quantization
por: Pham, Cuong, et al.
Publicado: (2024)
por: Pham, Cuong, et al.
Publicado: (2024)
ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
por: Yuan, Haochen, et al.
Publicado: (2024)
por: Yuan, Haochen, et al.
Publicado: (2024)
Finding Patterns in Ambiguity: Interpretable Stress Testing in the Decision~Boundary
por: Gomes, Inês, et al.
Publicado: (2024)
por: Gomes, Inês, et al.
Publicado: (2024)
Tailored Forecasting from Short Time Series via Meta-learning
por: Norton, Declan A., et al.
Publicado: (2025)
por: Norton, Declan A., et al.
Publicado: (2025)
External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting
por: Li, Haoran, et al.
Publicado: (2025)
por: Li, Haoran, et al.
Publicado: (2025)
Embedding-Space Data Augmentation to Prevent Membership Inference Attacks in Clinical Time Series Forecasting
por: Fracarolli, Marius, et al.
Publicado: (2025)
por: Fracarolli, Marius, et al.
Publicado: (2025)
Retrieval-Augmented Water Level Forecasting for Everglades
por: Rangaraj, Rahuul, et al.
Publicado: (2025)
por: Rangaraj, Rahuul, et al.
Publicado: (2025)
Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting
por: Kayaalp, Mert, et al.
Publicado: (2025)
por: Kayaalp, Mert, et al.
Publicado: (2025)
Stress-Testing Capability Elicitation With Password-Locked Models
por: Greenblatt, Ryan, et al.
Publicado: (2024)
por: Greenblatt, Ryan, et al.
Publicado: (2024)
Understanding Test-Time Augmentation
por: Kimura, Masanari
Publicado: (2024)
por: Kimura, Masanari
Publicado: (2024)
MetaEformer: Unveiling and Leveraging Meta-patterns for Complex and Dynamic Systems Load Forecasting
por: Huang, Shaoyuan, et al.
Publicado: (2025)
por: Huang, Shaoyuan, et al.
Publicado: (2025)
Meta-learning to Address Data Shift in Time Series Classification
por: Myren, Samuel, et al.
Publicado: (2026)
por: Myren, Samuel, et al.
Publicado: (2026)
A Decomposable Forward Process in Diffusion Models for Time-Series Forecasting
por: Caldas, Francisco, et al.
Publicado: (2026)
por: Caldas, Francisco, et al.
Publicado: (2026)
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
por: Pham, Thien, et al.
Publicado: (2025)
por: Pham, Thien, et al.
Publicado: (2025)
Retrieval Augmented Time Series Forecasting
por: Tire, Kutay, et al.
Publicado: (2024)
por: Tire, Kutay, et al.
Publicado: (2024)
Retrieval Augmented Time Series Forecasting
por: Han, Sungwon, et al.
Publicado: (2025)
por: Han, Sungwon, et al.
Publicado: (2025)
EST-PRM: Stress-Testing Process Reward Models Before They Become Load-Bearing
por: Shihab, Ibne Farabi, et al.
Publicado: (2026)
por: Shihab, Ibne Farabi, et al.
Publicado: (2026)
KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators
por: Singh, Ashutosh, et al.
Publicado: (2024)
por: Singh, Ashutosh, et al.
Publicado: (2024)
CLASP: An online learning algorithm for Convex Losses And Squared Penalties
por: Ferreira, Ricardo N., et al.
Publicado: (2026)
por: Ferreira, Ricardo N., et al.
Publicado: (2026)
Ejemplares similares
-
Time Series Data Augmentation as an Imbalanced Learning Problem
por: Cerqueira, Vitor, et al.
Publicado: (2024) -
Online Data Augmentation for Forecasting with Deep Learning
por: Cerqueira, Vitor, et al.
Publicado: (2024) -
Simulating Biases for Interpretable Fairness in Offline and Online Classifiers
por: Inácio, Ricardo, et al.
Publicado: (2025) -
ModelRadar: Aspect-based Forecast Evaluation
por: Cerqueira, Vitor, et al.
Publicado: (2025) -
Forecasting with Deep Learning: Beyond Average of Average of Average Performance
por: Cerqueira, Vitor, et al.
Publicado: (2024)