Copertina

T-Rep: Representation Learning for Time Series using Time-Embeddings

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Fraikin, Archibald, Bennetot, Adrien, Allassonnière, Stéphanie
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection
di: Senellart, Agathe, et al.
Pubblicazione: (2026)

Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning
di: Huang, Chun-Kai, et al.
Pubblicazione: (2024)

Learning to Embed Time Series Patches Independently
di: Lee, Seunghan, et al.
Pubblicazione: (2023)

TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
di: Chang, Ching, et al.
Pubblicazione: (2023)

Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning
di: Fu, En, et al.
Pubblicazione: (2024)

STEP: Learning STructured Embeddings for Progressive Time Series
di: Thil, Lucas, et al.
Pubblicazione: (2026)

Time Series Representation Models
di: Leppich, Robert, et al.
Pubblicazione: (2024)

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
di: Cortés, Adrien, et al.
Pubblicazione: (2025)

Universal Time-Series Representation Learning: A Survey
di: Trirat, Patara, et al.
Pubblicazione: (2024)

TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
di: Piao, Xihao, et al.
Pubblicazione: (2026)

On the Regularization of Learnable Embeddings for Time Series Forecasting
di: Butera, Luca, et al.
Pubblicazione: (2024)

Toward Physics-guided Time Series Embedding
di: Hu, Jiaxi, et al.
Pubblicazione: (2024)

Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
di: Kim, Byunghyun, et al.
Pubblicazione: (2024)

Learning Unified Representations of Normalcy for Time Series Anomaly Detection
di: Sarker, Prithul, et al.
Pubblicazione: (2026)

Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series
di: Chang, Ching, et al.
Pubblicazione: (2024)

Time Series Representation Learning with Supervised Contrastive Temporal Transformer
di: Liu, Yuansan, et al.
Pubblicazione: (2024)

Multi-Knowledge Fusion Network for Time Series Representation Learning
di: Sakhinana, Sagar Srinivas, et al.
Pubblicazione: (2024)

ABBA-VSM: Time Series Classification using Symbolic Representation on the Edge
di: Kanatbekova, Meerzhan, et al.
Pubblicazione: (2024)

Temporal Dynamic Embedding for Irregularly Sampled Time Series
di: Kim, Mincheol, et al.
Pubblicazione: (2025)

Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing
di: Jalali, Amin, et al.
Pubblicazione: (2025)

VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
di: Cencillo, Alberto D., et al.
Pubblicazione: (2026)

Attention as Robust Representation for Time Series Forecasting
di: Niu, PeiSong, et al.
Pubblicazione: (2024)

NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining
di: Lin, Chenguo, et al.
Pubblicazione: (2023)

Test Time Learning for Time Series Forecasting
di: Christou, Panayiotis, et al.
Pubblicazione: (2024)

Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections
di: Demirel, Berken Utku, et al.
Pubblicazione: (2025)

Non-Stationarity in the Embedding Space of Time Series Foundation Models
di: Choi, Jinmyeong, et al.
Pubblicazione: (2026)

A Time-Series Foundation Model by Universal Delay Embedding
di: Wang, Zijian, et al.
Pubblicazione: (2025)

PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse
di: Vaaras, Einari, et al.
Pubblicazione: (2024)

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
di: Park, Jinju, et al.
Pubblicazione: (2026)

MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations
di: Kim, Namwoo, et al.
Pubblicazione: (2025)

TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
di: Gao, Wentao, et al.
Pubblicazione: (2024)

Laplacian Convolutional Representation for Traffic Time Series Imputation
di: Chen, Xinyu, et al.
Pubblicazione: (2022)

MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant Features
di: Li, Huayu, et al.
Pubblicazione: (2023)

Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series Representation Learning
di: Sakhinana, Sagar Srinivas, et al.
Pubblicazione: (2024)

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series
di: Wang, Jia, et al.
Pubblicazione: (2025)

Higher-order Cross-structural Embedding Model for Time Series Analysis
di: Lin, Guancen, et al.
Pubblicazione: (2024)

TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding
di: Zhang, Haochuan, et al.
Pubblicazione: (2025)

EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions
di: Chakraborty, Sanjay, et al.
Pubblicazione: (2024)

D2Vformer: A Flexible Time Series Prediction Model Based on Time Position Embedding
di: Song, Xiaobao, et al.
Pubblicazione: (2024)

MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
di: Xu, Xianyong, et al.
Pubblicazione: (2026)