Cover Image

Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gao, Zijun, Liu, Haibao, Li, Lingbo
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Data Augmentation for Multivariate Time Series Classification: An Experimental Study
by: Ilbert, Romain, et al.
Published: (2024)

An Empirical Study: Extensive Deep Temporal Point Process
by: Lin, Haitao, et al.
Published: (2021)

A Comprehensive Survey on Data Augmentation
by: Wang, Zaitian, et al.
Published: (2024)

Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data
by: Niu, Wenjin, et al.
Published: (2024)

Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification
by: Liu, Ziyu, et al.
Published: (2024)

Deep Time Series Models: A Comprehensive Survey and Benchmark
by: Wang, Yuxuan, et al.
Published: (2024)

Early Classification of Time Series: A Survey and Benchmark
by: Renault, Aurélien, et al.
Published: (2024)

AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification
by: Chen, Yuxuan, et al.
Published: (2025)

OATS: Online Data Augmentation for Time Series Foundation Models
by: Deng, Junwei, et al.
Published: (2026)

An Ensemble Framework for Explainable Geospatial Machine Learning Models
by: Liu, Lingbo
Published: (2024)

Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study
by: Wang, Yuxuan, et al.
Published: (2025)

Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey
by: Ye, Jiexia, et al.
Published: (2024)

Time Series Data Augmentation as an Imbalanced Learning Problem
by: Cerqueira, Vitor, et al.
Published: (2024)

Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments
by: Arunan, Anushiya, et al.
Published: (2025)

Retrieval-Augmented Diffusion Models for Time Series Forecasting
by: Liu, Jingwei, et al.
Published: (2024)

FAIM: Frequency-Aware Interactive Mamba for Time Series Classification
by: Zhang, Da, et al.
Published: (2025)

Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks
by: Forootani, Ali, et al.
Published: (2025)

Data Augmentation in Time Series Forecasting through Inverted Framework
by: Tan, Hongming, et al.
Published: (2025)

Tabular Data Generation Models: An In-Depth Survey and Performance Benchmarks with Extensive Tuning
by: Kindji, G. Charbel N., et al.
Published: (2024)

Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models
by: Liu, Xu, et al.
Published: (2025)

An Exploratory Study to Repurpose LLMs to a Unified Architecture for Time Series Classification
by: He, Hansen, et al.
Published: (2026)

Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
by: Cheng, Mingyue, et al.
Published: (2025)

A Unified Framework for Generative Data Augmentation: A Comprehensive Survey
by: Chen, Yunhao, et al.
Published: (2023)

A Comprehensive Survey on Imbalanced Data Learning
by: Gao, Xinyi, et al.
Published: (2025)

An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
by: Huang, Qiang, et al.
Published: (2024)

Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey
by: Taha, Kamal
Published: (2024)

Advancing Time Series Classification with Multimodal Language Modeling
by: Cheng, Mingyue, et al.
Published: (2024)

Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
by: Leites, José, et al.
Published: (2024)

Learning Soft Sparse Shapes for Efficient Time-Series Classification
by: Liu, Zhen, et al.
Published: (2025)

An End-to-End Model for Time Series Classification In the Presence of Missing Values
by: Yao, Pengshuai, et al.
Published: (2024)

A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective
by: Qiu, Xiangfei, et al.
Published: (2025)

Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting
by: Tao, Xiaoyu, et al.
Published: (2026)

TADA: Temporal Adversarial Data Augmentation for Time Series Data
by: Lee, Byeong Tak, et al.
Published: (2024)

RAxSS: Retrieval-Augmented Sparse Sampling for Explainable Variable-Length Medical Time Series Classification
by: Javadov, Aydin, et al.
Published: (2025)

Why Model Selection Fails in Time Series Forecasting: An Empirical Study of Instability Across Data Regimes
by: Akinci, Tahir Cetin, et al.
Published: (2026)

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning
by: Tao, Xiaoyu, et al.
Published: (2026)

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
by: Yang, Yiyuan, et al.
Published: (2024)

On Multivariate Financial Time Series Classification
by: Bournassenko, Grégory
Published: (2025)

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

Meta-learning to Address Data Shift in Time Series Classification
by: Myren, Samuel, et al.
Published: (2026)