On the Regularization of Learnable Embeddings for Time Series Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | Butera, Luca, De Felice, Giovanni, Cini, Andrea, Alippi, Cesare |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Why Do Time Series Models Need Long Context Windows?
by: Butera, Luca, et al.
Published: (2026)
by: Butera, Luca, et al.
Published: (2026)
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
by: Cini, Andrea, et al.
Published: (2023)
by: Cini, Andrea, et al.
Published: (2023)
Graph Deep Learning for Time Series Forecasting
by: Cini, Andrea, et al.
Published: (2023)
by: Cini, Andrea, et al.
Published: (2023)
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations
by: De Felice, Giovanni, et al.
Published: (2024)
by: De Felice, Giovanni, et al.
Published: (2024)
Relational Conformal Prediction for Correlated Time Series
by: Cini, Andrea, et al.
Published: (2025)
by: Cini, Andrea, et al.
Published: (2025)
Object-Centric Relational Representations for Image Generation
by: Butera, Luca, et al.
Published: (2023)
by: Butera, Luca, et al.
Published: (2023)
Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting
by: Moretti, Valentina, et al.
Published: (2025)
by: Moretti, Valentina, et al.
Published: (2025)
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
by: Marisca, Ivan, et al.
Published: (2024)
by: Marisca, Ivan, et al.
Published: (2024)
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
by: Jin, Ming, et al.
Published: (2023)
by: Jin, Ming, et al.
Published: (2023)
Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning
by: Marzi, Tommaso, et al.
Published: (2025)
by: Marzi, Tommaso, et al.
Published: (2025)
Graph State-Space Models and Latent Relational Inference
by: Zambon, Daniele, et al.
Published: (2023)
by: Zambon, Daniele, et al.
Published: (2023)
TimeOmni-VL: Unified Models for Time Series Understanding and Generation
by: Guan, Tong, et al.
Published: (2026)
by: Guan, Tong, et al.
Published: (2026)
Learning Latent Graph Structures and their Uncertainty
by: Manenti, Alessandro, et al.
Published: (2024)
by: Manenti, Alessandro, et al.
Published: (2024)
Feudal Graph Reinforcement Learning
by: Marzi, Tommaso, et al.
Published: (2023)
by: Marzi, Tommaso, et al.
Published: (2023)
FX-DARTS: Designing Topology-unconstrained Architectures with Differentiable Architecture Search and Entropy-based Super-network Shrinking
by: Rao, Xuan, et al.
Published: (2025)
by: Rao, Xuan, et al.
Published: (2025)
PIF: Anomaly detection via preference embedding
by: Leveni, Filippo, et al.
Published: (2025)
by: Leveni, Filippo, et al.
Published: (2025)
Hashing for Structure-based Anomaly Detection
by: Leveni, Filippo, et al.
Published: (2025)
by: Leveni, Filippo, et al.
Published: (2025)
Preference Isolation Forest for Structure-based Anomaly Detection
by: Leveni, Filippo, et al.
Published: (2025)
by: Leveni, Filippo, et al.
Published: (2025)
LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
by: Delibasoglu, Ibrahim, et al.
Published: (2024)
by: Delibasoglu, Ibrahim, et al.
Published: (2024)
Characteristic Root Analysis and Regularization for Linear Time Series Forecasting
by: Wang, Zheng, et al.
Published: (2025)
by: Wang, Zheng, et al.
Published: (2025)
Enhancing Time Series Forecasting via Logic-Inspired Regularization
by: Zhang, Jianqi, et al.
Published: (2025)
by: Zhang, Jianqi, et al.
Published: (2025)
Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
by: Kim, Byunghyun, et al.
Published: (2024)
by: Kim, Byunghyun, et al.
Published: (2024)
Learnable Koopman-Enhanced Transformer-Based Time Series Forecasting with Spectral Control
by: Forootani, Ali, et al.
Published: (2026)
by: Forootani, Ali, et al.
Published: (2026)
Over-squashing in Spatiotemporal Graph Neural Networks
by: Marisca, Ivan, et al.
Published: (2025)
by: Marisca, Ivan, et al.
Published: (2025)
Understanding Pooling in Graph Neural Networks
by: Grattarola, Daniele, et al.
Published: (2021)
by: Grattarola, Daniele, et al.
Published: (2021)
Goal-Oriented Time-Series Forecasting: Foundation Framework Design
by: Fechete, Luca-Andrei, et al.
Published: (2025)
by: Fechete, Luca-Andrei, et al.
Published: (2025)
FlexTSF: A Flexible Forecasting Model for Time Series with Variable Regularities
by: Xiao, Jingge, et al.
Published: (2024)
by: Xiao, Jingge, et al.
Published: (2024)
Time Series Foundation Models for Process Model Forecasting
by: Yu, Yongbo, et al.
Published: (2025)
by: Yu, Yongbo, et al.
Published: (2025)
Diffusion-based Time Series Forecasting for Sewerage Systems
by: Pearson, Nicholas A., et al.
Published: (2025)
by: Pearson, Nicholas A., et al.
Published: (2025)
DB2-TransF: All You Need Is Learnable Daubechies Wavelets for Time Series Forecasting
by: Gupta, Moulik, et al.
Published: (2025)
by: Gupta, Moulik, et al.
Published: (2025)
Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding
by: Shin, Donghwa, et al.
Published: (2025)
by: Shin, Donghwa, et al.
Published: (2025)
Time Series Forecastability Measures
by: Wang, Rui, et al.
Published: (2025)
by: Wang, Rui, et al.
Published: (2025)
Ellipsoidal Time Series Forecasting
by: Wang, Qilin
Published: (2025)
by: Wang, Qilin
Published: (2025)
Performative Time-Series Forecasting
by: Zhao, Zhiyuan, et al.
Published: (2023)
by: Zhao, Zhiyuan, et al.
Published: (2023)
CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variables
by: Zhou, Pengfei, et al.
Published: (2025)
by: Zhou, Pengfei, et al.
Published: (2025)
LightSAE: Parameter-Efficient and Heterogeneity-Aware Embedding for IoT Multivariate Time Series Forecasting
by: Ren, Yi, et al.
Published: (2025)
by: Ren, Yi, et al.
Published: (2025)
LETS Forecast: Learning Embedology for Time Series Forecasting
by: Majeedi, Abrar, et al.
Published: (2025)
by: Majeedi, Abrar, et al.
Published: (2025)
Test Time Learning for Time Series Forecasting
by: Christou, Panayiotis, et al.
Published: (2024)
by: Christou, Panayiotis, et al.
Published: (2024)
Divide et Calibra: Multiclass Local Calibration via Vector Quantization
by: Barbera, Cesare, et al.
Published: (2026)
by: Barbera, Cesare, et al.
Published: (2026)
Multiclass Local Calibration with the Jensen-Shannon Distance
by: Barbera, Cesare, et al.
Published: (2025)
by: Barbera, Cesare, et al.
Published: (2025)
Similar Items
-
Why Do Time Series Models Need Long Context Windows?
by: Butera, Luca, et al.
Published: (2026) -
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
by: Cini, Andrea, et al.
Published: (2023) -
Graph Deep Learning for Time Series Forecasting
by: Cini, Andrea, et al.
Published: (2023) -
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations
by: De Felice, Giovanni, et al.
Published: (2024) -
Relational Conformal Prediction for Correlated Time Series
by: Cini, Andrea, et al.
Published: (2025)