Forecasting with Deep Learning: Beyond Average of Average of Average Performance
Fuente:
arXiv
Saved in:
| Main Authors: | Cerqueira, Vitor, Roque, Luis, Soares, Carlos |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Online Data Augmentation for Forecasting with Deep Learning
by: Cerqueira, Vitor, et al.
Published: (2024)
by: Cerqueira, Vitor, et al.
Published: (2024)
ModelRadar: Aspect-based Forecast Evaluation
by: Cerqueira, Vitor, et al.
Published: (2025)
by: Cerqueira, Vitor, et al.
Published: (2025)
AverageTime: Enhance Long-Term Time Series Forecasting with Simple Averaging
by: Zhao, Gaoxiang, et al.
Published: (2024)
by: Zhao, Gaoxiang, et al.
Published: (2024)
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
by: Leites, José, et al.
Published: (2024)
by: Leites, José, et al.
Published: (2024)
Learning Conditional Averages
by: Bressan, Marco, et al.
Published: (2026)
by: Bressan, Marco, et al.
Published: (2026)
N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
by: Matos, Ricardo, et al.
Published: (2025)
by: Matos, Ricardo, et al.
Published: (2025)
Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting
by: Orlova, Elena, et al.
Published: (2022)
by: Orlova, Elena, et al.
Published: (2022)
Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine
by: Roque, Luis, et al.
Published: (2024)
by: Roque, Luis, et al.
Published: (2024)
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits
by: Morales-Brotons, Daniel, et al.
Published: (2024)
by: Morales-Brotons, Daniel, et al.
Published: (2024)
On Defining Neural Averaging
by: Lee, Su Hyeong, et al.
Published: (2025)
by: Lee, Su Hyeong, et al.
Published: (2025)
Model Averaging and Double Machine Learning
by: Ahrens, Achim, et al.
Published: (2024)
by: Ahrens, Achim, et al.
Published: (2024)
Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast
by: Neubauer, Lukas, et al.
Published: (2023)
by: Neubauer, Lukas, et al.
Published: (2023)
Efficient Agnostic Learning with Average Smoothness
by: Hanneke, Steve, et al.
Published: (2023)
by: Hanneke, Steve, et al.
Published: (2023)
Learning to Integrate Diffusion ODEs by Averaging the Derivatives
by: Liu, Wenze, et al.
Published: (2025)
by: Liu, Wenze, et al.
Published: (2025)
Adaptive Stochastic Weight Averaging
by: Demir, Caglar, et al.
Published: (2024)
by: Demir, Caglar, et al.
Published: (2024)
Parameter Averaging in Link Prediction
by: Sapkota, Rupesh, et al.
Published: (2025)
by: Sapkota, Rupesh, et al.
Published: (2025)
Impoola: The Power of Average Pooling for Image-Based Deep Reinforcement Learning
by: Trumpp, Raphael, et al.
Published: (2025)
by: Trumpp, Raphael, et al.
Published: (2025)
Meta-learning and Data Augmentation for Stress Testing Forecasting Models
by: Inácio, Ricardo, et al.
Published: (2024)
by: Inácio, Ricardo, et al.
Published: (2024)
Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction
by: Cerqueira, Vitor, et al.
Published: (2022)
by: Cerqueira, Vitor, et al.
Published: (2022)
Implicit Updates for Average-Reward Temporal Difference Learning
by: Kim, Hwanwoo, et al.
Published: (2025)
by: Kim, Hwanwoo, et al.
Published: (2025)
Bridging the Gap Between Average and Discounted TD Learning
by: Tian, Haoxing, et al.
Published: (2026)
by: Tian, Haoxing, et al.
Published: (2026)
Planning and Learning in Average Risk-aware MDPs
by: Wang, Weikai, et al.
Published: (2025)
by: Wang, Weikai, et al.
Published: (2025)
Averaging log-likelihoods in direct alignment
by: Grinsztajn, Nathan, et al.
Published: (2024)
by: Grinsztajn, Nathan, et al.
Published: (2024)
Input Adaptive Bayesian Model Averaging
by: Slavutsky, Yuli, et al.
Published: (2025)
by: Slavutsky, Yuli, et al.
Published: (2025)
When, Where and Why to Average Weights?
by: Ajroldi, Niccolò, et al.
Published: (2025)
by: Ajroldi, Niccolò, et al.
Published: (2025)
Sample Weight Averaging for Stable Prediction
by: Yu, Han, et al.
Published: (2025)
by: Yu, Han, et al.
Published: (2025)
Bandit Simulation for Average Reward Inference
by: Praharaj, Samya, et al.
Published: (2026)
by: Praharaj, Samya, et al.
Published: (2026)
Beyond Simple Averaging: Improving NLP Ensemble Performance with Topological-Data-Analysis-Based Weighting
by: Proskura, Polina, et al.
Published: (2024)
by: Proskura, Polina, et al.
Published: (2024)
DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs
by: Shrestha, Aayam, et al.
Published: (2020)
by: Shrestha, Aayam, et al.
Published: (2020)
Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
by: Theologitis, Michail, et al.
Published: (2024)
by: Theologitis, Michail, et al.
Published: (2024)
Performance of NPG in Countable State-Space Average-Cost RL
by: Murthy, Yashaswini, et al.
Published: (2024)
by: Murthy, Yashaswini, et al.
Published: (2024)
DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging
by: Chourasia, Prakash, et al.
Published: (2024)
by: Chourasia, Prakash, et al.
Published: (2024)
Averaging $n$-step Returns Reduces Variance in Reinforcement Learning
by: Daley, Brett, et al.
Published: (2024)
by: Daley, Brett, et al.
Published: (2024)
Provably Sample-Efficient Robust Reinforcement Learning with Average Reward
by: Roch, Zachary, et al.
Published: (2025)
by: Roch, Zachary, et al.
Published: (2025)
RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning
by: Hisaki, Yukinari, et al.
Published: (2024)
by: Hisaki, Yukinari, et al.
Published: (2024)
EVAL: EigenVector-based Average-reward Learning
by: Adamczyk, Jacob, et al.
Published: (2025)
by: Adamczyk, Jacob, et al.
Published: (2025)
Central Limit Theorems for Asynchronous Averaged Q-Learning
by: Liu, Xingtu
Published: (2025)
by: Liu, Xingtu
Published: (2025)
Beyond the Average: Distributional Causal Inference under Imperfect Compliance
by: Byambadalai, Undral, et al.
Published: (2025)
by: Byambadalai, Undral, et al.
Published: (2025)
ReModels: Quantile Regression Averaging models
by: Zakrzewski, Grzegorz, et al.
Published: (2024)
by: Zakrzewski, Grzegorz, et al.
Published: (2024)
Average-DICE: Stationary Distribution Correction by Regression
by: Che, Fengdi, et al.
Published: (2025)
by: Che, Fengdi, et al.
Published: (2025)
Similar Items
-
Online Data Augmentation for Forecasting with Deep Learning
by: Cerqueira, Vitor, et al.
Published: (2024) -
ModelRadar: Aspect-based Forecast Evaluation
by: Cerqueira, Vitor, et al.
Published: (2025) -
AverageTime: Enhance Long-Term Time Series Forecasting with Simple Averaging
by: Zhao, Gaoxiang, et al.
Published: (2024) -
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
by: Leites, José, et al.
Published: (2024) -
Learning Conditional Averages
by: Bressan, Marco, et al.
Published: (2026)