Copertina

The temporal overfitting problem with applications in wind power curve modeling

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Prakash, Abhinav, Tuo, Rui, Ding, Yu
Natura:	Preprint
Pubblicazione:	2020
Soggetti:	Applications Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data
di: López-Oriona, Ángel, et al.
Pubblicazione: (2024)

Spatio-temporal modelling of electric vehicle charging demand
di: Bouaachra, Kaoutar, et al.
Pubblicazione: (2026)

Bayesian temporal biclustering with applications to multi-subject neuroscience studies
di: Ricci, Federica Zoe, et al.
Pubblicazione: (2024)

Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
di: Koldasbayeva, Diana, et al.
Pubblicazione: (2025)

Estimating the age-conditioned average treatment effects curves: An application for assessing load-management strategies in the NBA
di: Nakamura-Sakai, Shinpei, et al.
Pubblicazione: (2024)

Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules
di: Wessel, Jakob Benjamin, et al.
Pubblicazione: (2024)

Agile gesture recognition for low-power applications: customisation for generalisation
di: Liu, Ying, et al.
Pubblicazione: (2024)

A comparison between geostatistical and machine learning models for spatio-temporal prediction of PM2.5 data
di: Mohamed, Zeinab, et al.
Pubblicazione: (2025)

A Gibbs posterior sampler for inverse problem based on prior diffusion model
di: Giovannelli, Jean-François
Pubblicazione: (2026)

Classification problem in liability insurance using machine learning models: a comparative study
di: Qazvini, Marjan
Pubblicazione: (2024)

Prediction-powered Inference by Mixture of Experts
di: Gu, Yanwu, et al.
Pubblicazione: (2026)

Variable importance measure for spatial machine learning models with application to air pollution exposure prediction
di: Cheng, Si, et al.
Pubblicazione: (2024)

Ozone level forecasting in Mexico City with temporal features and interactions
di: Cerritos, J. M. Sánchez, et al.
Pubblicazione: (2024)

Local transfer learning Gaussian process modeling, with applications to surrogate modeling of expensive computer simulators
di: Wang, Xinming, et al.
Pubblicazione: (2024)

Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem
di: Esteghamati, Mohsen Zaker
Pubblicazione: (2025)

Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems
di: Wang, Yinsong, et al.
Pubblicazione: (2025)

I See, Therefore I Do: Estimating Causal Effects for Image Treatments
di: Thorat, Abhinav, et al.
Pubblicazione: (2024)

Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks
di: Zanetta, Francesco, et al.
Pubblicazione: (2024)

Dynamic landslide susceptibility mapping over recent three decades to uncover variations in landslide causes in subtropical urban mountainous areas
di: Ma, Peifeng, et al.
Pubblicazione: (2023)

Can artificial intelligence predict clinical trial outcomes?
di: Jin, Shuyi, et al.
Pubblicazione: (2024)

Spatio-temporal DeepKriging in PyTorch: A Supplementary Application to Precipitation Data for Interpolation and Probabilistic Forecasting
di: Nag, Pratik
Pubblicazione: (2025)

A two-step machine learning approach to statistical post-processing of weather forecasts for power generation
di: Baran, Ágnes, et al.
Pubblicazione: (2022)

Variational Bayesian surrogate modelling with application to robust design optimisation
di: Archbold, Thomas A., et al.
Pubblicazione: (2024)

Data is missing again -- Reconstruction of power generation data using $k$-Nearest Neighbors and spectral graph theory
di: Pierrot, Amandine, et al.
Pubblicazione: (2024)

Confidence interval estimation of mixed oil length with conditional diffusion model
di: Yang, Yanfeng, et al.
Pubblicazione: (2024)

Missing data imputation for noisy time-series data and applications in healthcare
di: Le, Lien P., et al.
Pubblicazione: (2024)

Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression
di: Wang, Ding, et al.
Pubblicazione: (2024)

Estimation of instrument and noise parameters for inverse problem based on prior diffusion model
di: Giovannelli, Jean-François
Pubblicazione: (2026)

Mixture-of-experts Wishart model for covariance matrices with an application to Cancer drug screening
di: Mai, The Tien, et al.
Pubblicazione: (2026)

Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study
di: Liu, Xueqing, et al.
Pubblicazione: (2023)

Bayesian Semi-supervised Multi-category Classification under Nonparanormality
di: Zhu, Rui, et al.
Pubblicazione: (2020)

Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data
di: Xie, Rui, et al.
Pubblicazione: (2023)

Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
di: Pryce, Matthew, et al.
Pubblicazione: (2026)

A variational inference framework for inverse problems
di: Maestrini, Luca, et al.
Pubblicazione: (2021)

Revisiting PCA for time series reduction in temporal dimension
di: Gao, Jiaxin, et al.
Pubblicazione: (2024)

Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions
di: Lin, Gefei, et al.
Pubblicazione: (2026)

Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models
di: Benitez-Aurioles, Jose, et al.
Pubblicazione: (2026)

Meta-models for transfer learning in source localisation
di: Bull, Lawrence A., et al.
Pubblicazione: (2023)

Identifiability and amortized inference limitations in Kuramoto models
di: Hannula, Emma, et al.
Pubblicazione: (2026)

Federated One-Shot Ensemble Clustering
di: Duan, Rui, et al.
Pubblicazione: (2024)