ArticlePloS one2021
An empirical survey of data augmentation for time series classification with neural networks.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
86 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020.Frontiers in physiology · 2021Pooled it
- Automated Cerebral Edema Detection using Electroencephalography in Post-cardiac Arrest Patients.Neurocritical care · 2026Article
- Level-Aware Residual Mixup for Defect Classification in Material Extrusion with Limited Print Jobs.Materials (Basel, Switzerland) · 2026Article
- Forecasting extreme temperature events by Hankel-augmented contrastive learning.National science review · 2026Article
- SleepStageNet: A Lightweight and Explainable Deep Learning Architecture for Multi-Channel Sleep Staging.Brain sciences · 2026Article
- A robust deep learning framework for automated fetal behavioral state classification: leveraging multi-center datasets to improve antepartum heart rate monitoring.Biomedical signal processing and control · 2026Article
- CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification.Scientific reports · 2026Article
- An Evaluation of Pretrained Generative Models for Augmenting Small Health Data: Comparative Modeling Study.Journal of medical Internet research · 2026Article
- Recognition of Human Gait Under Asymmetric Loading.Sensors (Basel, Switzerland) · 2026Article
- Depression Detection from Three-Channel Resting-State EEG Using a Hybrid Conv1D and Spectral-Statistical Fusion Model.Sensors (Basel, Switzerland) · 2026Article
- Action-Aware Multimodal Wavelet Fusion Network for Quantitative Elbow Motor Function Assessment Using sEMG and Robotic Kinematics.Sensors (Basel, Switzerland) · 2026Article
- Selective Motor Entropy Modulation and Targeted Augmentation for the Identification of Parkinsonian Gait Patterns Using Multimodal Gait Analysis.Life (Basel, Switzerland) · 2026Article
- Portable Dual-Mode Biosensor for Quantitative Determination ofBiosensors · 2026Article
- Benchmark-grounded self-supervised contrastive learning for AMI-based distribution grid monitoring.Frontiers in artificial intelligence · 2026Article
- Article
- AI-enhanced non-invasive monitoring of critical biochemical analytes in perioperative care: a review of signal processing and clinical applications.Frontiers in physiology · 2026Review
- A Novel Fabric Strain Sensor Array with Hybrid Deep Learning for Accurate Knee Movement Recognition.Micromachines · 2025Article
- Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study.BioData mining · 2025Article
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.