Copertina

IRNN: Innovation-driven Recurrent Neural Network for Time-Series Data Modeling and Prediction

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhou, Yifan, Wang, Yibo, Shang, Chao
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series
di: Piao, Chengzhe, et al.
Pubblicazione: (2024)

Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation
di: Qian, Linglong, et al.
Pubblicazione: (2024)

Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks
di: Shen, Rujia, et al.
Pubblicazione: (2024)

Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Data
di: Yen, Wei-hsiang, et al.
Pubblicazione: (2025)

GPU Memory Requirement Prediction for Deep Learning Task Based on Bidirectional Gated Recurrent Unit Optimization Transformer
di: Wang, Chao, et al.
Pubblicazione: (2025)

Towards Neural Scaling Laws for Time Series Foundation Models
di: Yao, Qingren, et al.
Pubblicazione: (2024)

Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification
di: Qu, Jintao, et al.
Pubblicazione: (2025)

BRATI: Bidirectional Recurrent Attention for Time-Series Imputation
di: Collado-Villaverde, Armando, et al.
Pubblicazione: (2025)

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
di: Wang, Wenxuan, et al.
Pubblicazione: (2025)

Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks
di: Sajadi, Pouyan, et al.
Pubblicazione: (2024)

Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability
di: Xu, Kunpeng, et al.
Pubblicazione: (2024)

Context Neural Networks: A Scalable Multivariate Model for Time Series Forecasting
di: Sriramulu, Abishek, et al.
Pubblicazione: (2024)

Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery
di: Zheng, Yuhan, et al.
Pubblicazione: (2024)

Neural Predictive Control to Coordinate Discrete- and Continuous-Time Models for Time-Series Analysis with Control-Theoretical Improvements
di: Li, Haoran, et al.
Pubblicazione: (2025)

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
di: Jin, Ming, et al.
Pubblicazione: (2023)

Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation
di: Wang, Wenxuan, et al.
Pubblicazione: (2025)

Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting
di: Liu, Yiqian, et al.
Pubblicazione: (2026)

EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
di: Zhou, Xinxing, et al.
Pubblicazione: (2026)

Renaissance of RNNs in Streaming Clinical Time Series: Compact Recurrence Remains Competitive with Transformers
di: Tong, Ran, et al.
Pubblicazione: (2025)

Generative System Dynamics in Recurrent Neural Networks
di: Casoni, Michele, et al.
Pubblicazione: (2025)

PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes
di: Zhou, Yiming, et al.
Pubblicazione: (2026)

Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification
di: Farsang, Mónika, et al.
Pubblicazione: (2026)

Stock Price Prediction using Multi-Faceted Information based on Deep Recurrent Neural Networks
di: Shahbandari, Lida, et al.
Pubblicazione: (2024)

Axiomatization of Gradient Smoothing in Neural Networks
di: Zhou, Linjiang, et al.
Pubblicazione: (2024)

Long Input Sequence Network for Long Time Series Forecasting
di: Ma, Chao, et al.
Pubblicazione: (2024)

Generalizability of Memorization Neural Networks
di: Yu, Lijia, et al.
Pubblicazione: (2024)

Physics-Inspired Spatial Temporal Graph Neural Networks for Predicting Industrial Chain Resilience
di: Wang, Bicheng, et al.
Pubblicazione: (2025)

Time Series Imputation with Multivariate Radial Basis Function Neural Network
di: Jung, Chanyoung, et al.
Pubblicazione: (2024)

Predicting Time Series of Networked Dynamical Systems without Knowing Topology
di: Ding, Yanna, et al.
Pubblicazione: (2024)

Large Language Models are Few-shot Multivariate Time Series Classifiers
di: Chen, Yakun, et al.
Pubblicazione: (2025)

Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Ability
di: Yu, Lijia, et al.
Pubblicazione: (2025)

Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
di: Wu, Shunyu, et al.
Pubblicazione: (2025)

Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers
di: Ni, Haowei, et al.
Pubblicazione: (2024)

CAPS: Unifying Attention, Recurrence, and Alignment in Transformer-based Time Series Forecasting
di: Pati, Viresh, et al.
Pubblicazione: (2026)

BARNN: A Bayesian Autoregressive and Recurrent Neural Network
di: Coscia, Dario, et al.
Pubblicazione: (2025)

Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling
di: Zhao, Sinong, et al.
Pubblicazione: (2024)

Nested Spatio-Temporal Time Series Forecasting
di: Ai, Yinghao, et al.
Pubblicazione: (2026)

ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data
di: Li, Mengxuan, et al.
Pubblicazione: (2025)

TA-RNN: an Attention-based Time-aware Recurrent Neural Network Architecture for Electronic Health Records
di: Olaimat, Mohammad Al, et al.
Pubblicazione: (2024)

TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis
di: Wang, Shiyu, et al.
Pubblicazione: (2024)