Image de couverture de livre

Diff-MTS: Temporal-Augmented Conditional Diffusion-based AIGC for Industrial Time Series Towards the Large Model Era

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ren, Lei, Wang, Haiteng, Laili, Yuanjun
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models
par: Ren, Lei, et autres
Publié: (2024)

UniMTS: Unified Pre-training for Motion Time Series
par: Zhang, Xiyuan, et autres
Publié: (2024)

MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant Features
par: Li, Huayu, et autres
Publié: (2023)

SimDiff: Simpler Yet Better Diffusion Model for Time Series Point Forecasting
par: Ding, Hang, et autres
Publié: (2025)

Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models
par: Hou, Xuan, et autres
Publié: (2025)

Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model
par: Tian, Yuxing, et autres
Publié: (2024)

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
par: Yang, Yiyuan, et autres
Publié: (2024)

A Time-Series Data Augmentation Model through Diffusion and Transformer Integration
par: Zhang, Yuren, et autres
Publié: (2025)

MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation
par: Zhou, Jianping, et autres
Publié: (2024)

TS-MLLM: A Multi-Modal Large Language Model-based Framework for Industrial Time-Series Big Data Analysis
par: Wang, Haiteng, et autres
Publié: (2026)

T-LLM: Teaching Large Language Models to Forecast Time Series via Temporal Distillation
par: Guo, Suhan, et autres
Publié: (2026)

Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection
par: Baumgartner, David, et autres
Publié: (2026)

Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
par: Zhang, Xu, et autres
Publié: (2026)

TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
par: Suh, Namjoon, et autres
Publié: (2024)

DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation
par: Gao, Yuan, et autres
Publié: (2024)

MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
par: Xu, Xianyong, et autres
Publié: (2026)

MTS: A Deep Reinforcement Learning Portfolio Management Framework with Time-Awareness and Short-Selling
par: Gu, Fengchen, et autres
Publié: (2025)

FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
par: Othman, Karim, et autres
Publié: (2026)

STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
par: Simethy, Gary, et autres
Publié: (2025)

DiLaDiff: Distilled Latent-Augmented Diffusion for Language Modeling
par: Lemercier, Jean-Marie, et autres
Publié: (2026)

TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
par: Lee, Geon, et autres
Publié: (2025)

Semantically-Guided Inference for Conditional Diffusion Models: Enhancing Covariate Consistency in Time Series Forecasting
par: Ding, Rui, et autres
Publié: (2025)

TimeGraphs: Graph-based Temporal Reasoning
par: Maheshwari, Paridhi, et autres
Publié: (2024)

TADA: Temporal Adversarial Data Augmentation for Time Series Data
par: Lee, Byeong Tak, et autres
Publié: (2024)

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
par: Jin, Ming, et autres
Publié: (2023)

DiffGRM: Diffusion-based Generative Recommendation Model
par: Liu, Zhao, et autres
Publié: (2025)

Diffusion-based Time Series Forecasting for Sewerage Systems
par: Pearson, Nicholas A., et autres
Publié: (2025)

DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
par: Wang, Zhixian, et autres
Publié: (2023)

RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting
par: Lai, Chih-Yu, et autres
Publié: (2025)

The Rise of Diffusion Models in Time-Series Forecasting
par: Meijer, Caspar, et autres
Publié: (2024)

Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
par: Srinivas, Sakhinana Sagar, et autres
Publié: (2024)

FaultDiffusion: Few-Shot Fault Time Series Generation with Diffusion Model
par: Xu, Yi, et autres
Publié: (2025)

TF-CoDiT: Conditional Time Series Synthesis with Diffusion Transformers for Treasury Futures
par: Zhang, Yingxiao, et autres
Publié: (2026)

Nested Spatio-Temporal Time Series Forecasting
par: Ai, Yinghao, et autres
Publié: (2026)

EEGAgent: A Unified Framework for Automated EEG Analysis Using Large Language Models
par: Zhao, Sha, et autres
Publié: (2025)

Phys-Diff: A Physics-Inspired Latent Diffusion Model for Tropical Cyclone Forecasting
par: Liu, Lei, et autres
Publié: (2026)

Retrieval Augmented Time Series Forecasting
par: Tire, Kutay, et autres
Publié: (2024)

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding
par: Ding, Kuiye, et autres
Publié: (2025)

RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data
par: Yeh, Chin-Chia Michael, et autres
Publié: (2024)

Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
par: Sergeev, Fedor, et autres
Publié: (2024)