# Data Augmentation techniques in time series domain: A survey and taxonomy

arXiv: https://arxiv.org/abs/2206.13508 · HTML: https://mlsanity.com/paper/2206.13508/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 133 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 30 of 56)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Towards Principled Methods for Training Generative Adversarial Networks](https://mlsanity.com/paper/1701.04862/) (2017)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis](https://mlsanity.com/paper/1904.01310/) (2019)
- [Time Series Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2002.12478/) (2020)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)

## Built on by (most cited first, 30 of 41)

- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2306.10125/) (2023)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification](https://mlsanity.com/paper/2409.06290/) (2024)
- [Relaxed Equivariance via Multitask Learning](https://mlsanity.com/paper/2410.17878/) (2024)
- [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024)
- [Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey](https://mlsanity.com/paper/2310.10060/) (2023)
- [Macroeconomic Forecasting with Large Language Models](https://mlsanity.com/paper/2407.00890/) (2024)
- [Time-Causal VAE: Robust Financial Time Series Generator](https://mlsanity.com/paper/2411.02947/) (2024)
- [A Survey of Transformer Enabled Time Series Synthesis](https://mlsanity.com/paper/2406.02322/) (2024)
- [WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch](https://mlsanity.com/paper/2502.05783/) (2025)
- [D3A-TS: Denoising-Driven Data Augmentation in Time Series](https://mlsanity.com/paper/2312.05550/) (2023)
- [ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting](https://mlsanity.com/paper/2409.06282/) (2024)
- [Synthetic Data Generation for Augmenting Small Samples](https://mlsanity.com/paper/2501.18741/) (2025)
- [PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2504.17163/) (2025)
- [Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing](https://mlsanity.com/paper/2407.01644/) (2024)
- [Data Augmentation of Time-Series Data in Human Movement Biomechanics: A Scoping Review](https://mlsanity.com/paper/2504.03334/) (2025)
- [A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning](https://mlsanity.com/paper/2507.01976/) (2025)
- [Explainable Anomaly Detection: Counterfactual driven What-If Analysis](https://mlsanity.com/paper/2408.11935/) (2024)
- [Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification](https://mlsanity.com/paper/2411.18456/) (2024)
- [A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic Segmentation](https://mlsanity.com/paper/2505.19159/) (2025)
- [On-the-Fly Data Augmentation via Gradient-Guided and Sample-Aware Influence Estimation](https://mlsanity.com/paper/2510.00434/) (2025)
- [TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare](https://mlsanity.com/paper/2602.07033/) (2026)
- [Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models](https://mlsanity.com/paper/2603.00629/) (2026)
- [Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing System](https://mlsanity.com/paper/2306.06268/) (2023)
- [Improving the Security of Smartwatch Payment with Deep Learning](https://mlsanity.com/paper/2307.05437/) (2023)
- [Collinear datasets augmentation using Procrustes validation sets](https://mlsanity.com/paper/2312.04911/) (2023)
- [Generating Synthetic Time Series Data for Cyber-Physical Systems](https://mlsanity.com/paper/2404.08601/) (2024)
- [Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments](https://mlsanity.com/paper/2406.04890/) (2024)
- [HSDA: High-frequency Shuffle Data Augmentation for Bird's-Eye-View Map Segmentation](https://mlsanity.com/paper/2412.06127/) (2024)
- [Swarm Characteristic Classification using Robust Neural Networks with Optimized Controllable Inputs](https://mlsanity.com/paper/2502.03619/) (2025)

## Abstract (arXiv)

With the latest advances in Deep Learning-based generative models, it has not taken long to take advantage of their remarkable performance in the area of time series. Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. These features are not usually abundant in the real world, where they are usually limited and often have constraints that must be guaranteed. Therefore, an effective way to increase the amount of data is by using Data Augmentation techniques, either by adding noise or permutations and by generating new synthetic data. This work systematically reviews the current state-of-the-art in the area to provide an overview of all available algorithms and proposes a taxonomy of the most relevant research. The efficiency of the different variants will be evaluated as a central part of the process, as well as the different metrics to evaluate the performance and the main problems concerning each model will be analysed. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.