Copertina

IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Oppel, Heiko, Munz, Michael
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models
di: Oppel, Heiko, et al.
Pubblicazione: (2025)

Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs
di: Oppel, Heiko, et al.
Pubblicazione: (2025)

Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation
di: Spilz, Andreas, et al.
Pubblicazione: (2025)

Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control
di: Carvalho, Tânia, et al.
Pubblicazione: (2022)

Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
di: Huk, David, et al.
Pubblicazione: (2026)

STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting
di: Wang, Yulong, et al.
Pubblicazione: (2025)

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
di: Soni, Juhi, et al.
Pubblicazione: (2025)

MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation
di: Zhou, Jianping, et al.
Pubblicazione: (2024)

Latent Laplace Diffusion for Irregular Multivariate Time Series
di: You, Zinuo, et al.
Pubblicazione: (2026)

Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
di: Zhang, S., et al.
Pubblicazione: (2024)

Treating Brain-inspired Memories as Priors for Diffusion Model to Forecast Multivariate Time Series
di: Wang, Muyao, et al.
Pubblicazione: (2024)

Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependencies
di: Xie, Yongzheng, et al.
Pubblicazione: (2025)

Time Series Foundation Models for Multivariate Financial Time Series Forecasting
di: Marconi, Ben A.
Pubblicazione: (2025)

Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
di: Li, Siyang, et al.
Pubblicazione: (2024)

Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation
di: Liang, Guojun, et al.
Pubblicazione: (2024)

On Multivariate Financial Time Series Classification
di: Bournassenko, Grégory
Pubblicazione: (2025)

FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
di: Li, Runze, et al.
Pubblicazione: (2025)

SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting
di: Jiang, Yue, et al.
Pubblicazione: (2024)

VE: Modeling Multivariate Time Series Correlation with Variate Embedding
di: Wang, Shangjiong, et al.
Pubblicazione: (2024)

Pyramidal Hidden Markov Model For Multivariate Time Series Forecasting
di: Huang, YeXin
Pubblicazione: (2023)

Transformer Modeling for Both Scalability and Performance in Multivariate Time Series
di: Lee, Hunjae, et al.
Pubblicazione: (2025)

PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting
di: Ma, Jiaming, et al.
Pubblicazione: (2026)

Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
di: Ho, Thi Kieu Khanh, et al.
Pubblicazione: (2023)

A SSM is Polymerized from Multivariate Time Series
di: Wu, Haixiang
Pubblicazione: (2024)

The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification
di: Middlehurst, Matthew, et al.
Pubblicazione: (2026)

Algorithmic Recourse in Abnormal Multivariate Time Series
di: Han, Xiao, et al.
Pubblicazione: (2023)

Stationarity Exploration for Multivariate Time Series Forecasting
di: Liu, Hao, et al.
Pubblicazione: (2025)

SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
di: Rezaei, Mohammad R., et al.
Pubblicazione: (2026)

A Simple State Space Model Excels at Multivariate Time Series Classification
di: Saadatmand, Hassan, et al.
Pubblicazione: (2026)

A Decomposition-based State Space Model for Multivariate Time-Series Forecasting
di: Nagashima, Shunya, et al.
Pubblicazione: (2026)

Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting
di: Qiu, Xiangfei, et al.
Pubblicazione: (2026)

User-friendly Foundation Model Adapters for Multivariate Time Series Classification
di: Feofanov, Vasilii, et al.
Pubblicazione: (2024)

Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series
di: Liu, Mingzhu, et al.
Pubblicazione: (2024)

OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data
di: Langer, Patrick, et al.
Pubblicazione: (2025)

SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
di: Han, Lu, et al.
Pubblicazione: (2024)

Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
di: Yu, Guoqi, et al.
Pubblicazione: (2024)

Transparent Networks for Multivariate Time Series
di: Kim, Minkyu, et al.
Pubblicazione: (2024)

Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
di: Liu, Yiding, et al.
Pubblicazione: (2026)

ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
di: Cheng, Mingyue, et al.
Pubblicazione: (2024)

Multivariate Probabilistic Time Series Forecasting with Correlated Errors
di: Zheng, Vincent Zhihao, et al.
Pubblicazione: (2024)