Example: Totraina 1DVGGon theFiftyWordsdataset from theUCR Time Series Archive 2018with4xthe training dataset inJittering, use: python3 main.py --gpus=0 --dataset=CBF --preset_files --ucr2018 --normalize_input --train --save --jitter --augmentation_ratio=4 --model=vgg ...
tsai - Time Series Data Augmentation fastai还是一如既往的优秀! github.com/sdv-dev/SDV SDV中设计了gan和vae这类比较advance的方法,对于tabular gan的功能相当出色~ github.com/zzw-zwzhang/ 汇总的awesome系列 github.com/makcedward/n nlpaug里关于音频的增强也可以无缝用在时间序列数据上 waterprogramming.wordp...
AugmentTS :: Time Series Data Augmentation using Deep Generative Models - GitHub - DrSasanBarak/AugmentTS: AugmentTS :: Time Series Data Augmentation using Deep Generative Models
代码链接:github.com/salesforce/f 关键词:Time Series, Data augmentation, Representation Learning, Deep Learning, Reinforcement Learning 一句话总结全文:本文提出了一种基于库普曼算子理论的新模型,用于预测具有分布变化的时间序列。 研究内容:尽管最近深度学习在时间序列预测方面取得了成功,但这些方法无法扩展到许多数...
Time-Series Data Augmentation(时间序列数据增强) Temporal Contrasting(时间对比模块) Contextual Contrasting(上下文对比模块) Architecture of proposedTS-TCCmodel 4. Experimental Setup 介绍实验数据集 Human Activity Recognition (HAR)【人类活动识别数据集】、Sleep Stage Classification【睡眠阶段分类数据集】、Epilepsy...
8. SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation 作者：Ryu, Hyun*; Yoon, Sunjae; Yoon, Hee Suk; Yoon, Eunseop; Yoo, Chang D.关键词：谱域、时间序列数据增强 Code：github.com/Hyun-Ryu/sim...arXiv：arxiv.org/abs/2312.0579...9....
或者是:Time series contrastive learning with information-aware augmentations GitHub:https://github.com/chengw07/InfoTS AAAI 2023的论文。 摘要 近年来,人们提出了各种对比学习方法,并取得了显著的实证成功。对比学习方法虽然有效且普遍,但对时间序列数据的探索却较少。对比学习的一个关键组成部分是选择适当的增强...
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TS
或者是:Time series contrastive learning with information-aware augmentations GitHub:https://github.com/chengw07/InfoTS AAAI 2023的论文。 摘要 近年来,人们提出了各种对比学习方法,并取得了显著的实证成功。对比学习方法虽然有效且普遍,但对时间序列数据的探索却较少。对比学习的一个关键组成部分是选择适当的增强...
A Python package for time series augmentation. Contribute to arundo/tsaug development by creating an account on GitHub.