# Data augmentation using synthetic data for time series classification with deep residual networks

arXiv: https://arxiv.org/abs/1808.02455 · HTML: https://mlsanity.com/paper/1808.02455/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 30 of 38)

- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [Neighborhood Contrastive Learning Applied to Online Patient Monitoring](https://mlsanity.com/paper/2106.05142/) (2021)
- [Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention](https://mlsanity.com/paper/2405.01820/) (2024)
- [Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions](https://mlsanity.com/paper/2203.06852/) (2022)
- [On the Feasibility of Vision-Language Models for Time-Series Classification](https://mlsanity.com/paper/2412.17304/) (2024)
- [Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification](https://mlsanity.com/paper/2106.08848/) (2021)
- [End-to-End Signal Classification in Signed Cumulative Distribution Transform Space](https://mlsanity.com/paper/2205.00348/) (2022)
- [Towards Diverse and Coherent Augmentation for Time-Series Forecasting](https://mlsanity.com/paper/2303.14254/) (2023)
- [Text Reinforcement for Multimodal Time Series Forecasting](https://mlsanity.com/paper/2509.00687/) (2025)
- [ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging](https://mlsanity.com/paper/2309.16353/) (2023)
- [Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities](https://mlsanity.com/paper/2402.01071/) (2024)
- [DTW-Merge: A Novel Data Augmentation Technique for Time Series Classification](https://mlsanity.com/paper/2103.01119/) (2021)
- [Finding Foundation Models for Time Series Classification with a PreText Task](https://mlsanity.com/paper/2311.14534/) (2023)
- [Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques](https://mlsanity.com/paper/2506.07612/) (2025)
- [Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review](https://mlsanity.com/paper/2506.12322/) (2025)
- [IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance](https://mlsanity.com/paper/2110.07460/) (2021)
- [Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices](https://mlsanity.com/paper/2402.03714/) (2024)
- [WaveletDiff: Multilevel Wavelet Diffusion For Time Series Generation](https://mlsanity.com/paper/2510.11839/) (2025)
- [Efficient Classification with Counterfactual Reasoning and Active Learning](https://mlsanity.com/paper/2207.12086/) (2022)
- [Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics](https://mlsanity.com/paper/2212.02622/) (2022)
- [DTWSSE: Data Augmentation with a Siamese Encoder for Time Series](https://mlsanity.com/paper/2108.09885/) (2021)
- [Don't overfit the history -- Recursive time series data augmentation](https://mlsanity.com/paper/2207.02891/) (2022)
- [Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models](https://mlsanity.com/paper/2603.00629/) (2026)
- [Machine Learning Applications to Kronian Magnetospheric Reconnection Classification](https://mlsanity.com/paper/2104.00496/) (2021)
- [Short-Term Stock Price-Trend Prediction Using Meta-Learning](https://mlsanity.com/paper/2105.13599/) (2021)
- [Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data](https://mlsanity.com/paper/2302.10490/) (2023)
- [Fractal interpolation in the context of prediction accuracy optimization](https://mlsanity.com/paper/2403.00403/) (2024)

## Abstract (arXiv)

Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike in image recognition problems, data augmentation techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted. In this paper, we fill this gap by investigating the application of a recently proposed data augmentation technique based on the Dynamic Time Warping distance, for a deep learning model for TSC. To evaluate the potential of augmenting the training set, we performed extensive experiments using the UCR TSC benchmark. Our preliminary experiments reveal that data augmentation can drastically increase deep CNN's accuracy on some datasets and significantly improve the deep model's accuracy when the method is used in an ensemble approach.