MSD-IRIMAS/Augmenting-TSC-Elastic-Averaging
Augmenting Time Series Datasets with Weighted Elastic Barycenter Averaging
This tool helps data scientists and machine learning engineers enhance their time series classification models when they have limited training data. By taking existing time series data, it generates new, synthetic time series examples that are similar but varied, using advanced averaging techniques. The output is an augmented dataset that can improve the performance of classification algorithms.
No commits in the last 6 months.
Use this if you are building a time series classification model and your existing dataset is too small or sparse to train a robust model effectively.
Not ideal if your dataset is already large and diverse, or if you are not working with time series classification problems.
Stars
10
Forks
3
Language
Python
License
GPL-3.0
Category
Last pushed
Jun 02, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/MSD-IRIMAS/Augmenting-TSC-Elastic-Averaging"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
TorchIO-project/torchio
Medical imaging processing for AI applications.
aleju/imgaug
Image augmentation for machine learning experiments.
makcedward/nlpaug
Data augmentation for NLP
mdbloice/Augmentor
Image augmentation library in Python for machine learning.
BloodAxe/pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming