A sample code of data augmentation methods for wearable sensor data (time-series data)

A Python package for time series augmentation

An example of time series augmentation methods with Keras

Generation and evaluation of synthetic time series datasets (also, augmentations, visualizations, a collection of popular datasets) NeurIPS'24

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

TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation

A curated list of time series augmentation resources.

TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

AugmentTS :: Time Series Data Augmentation using Deep Generative Models

The repository provides code implementations and illustrative examples of NeurIPS 2023 paper, Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning.

Official code for "SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation", AAAI 2024.

Code for KDD 2025 paper "FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification"

ECG time-series augmentations library

Data augmentation for multivariate time series classification

Time Series Contrastive Learning with Information-Aware Augmentations (Code)

AutoTCL and Parametric Augmentation for Time Series Contrastive Learning(ICLR2024)

Code For Paper "Bridging the Spectrum Gap: Mid‑Frequency Augmentation and Key‑Frequency Mining for Multivariate Time Series"

This repository contains all the source code needed to reproduce the experiments or review the results obtained in the research paper "D3A-TS: Denoising-Driven Data Augmentation in Time Series"

WWGAN (Worm Wasserstein GAN), a Generative Adversarial Network (GAN) for time-series data augmentation

This is the implementation for the paper "Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning"

Deep Learning Adventures. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

Towards Diverse and Coherent Augmentation for Time-Series Forecasting

ICPR2022: Dynamic Data Augmentation with Gating Networks for Time Series Recognition

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

Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting

Time Series Data Augmentation on Seismic Data

🌟Learning Hierarchical Time Series Data Augmentation Invariances via Contrastive Supervision for Human Activity Recognition

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