Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning

• URL: http://arxiv.org/abs/2309.13439v2
• Date: Thu, 21 Dec 2023 09:00:09 GMT
• Title: Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
• Authors: Berken Utku Demirel and Christian Holz
• Abstract summary: We propose a novel data augmentation method for quasi-periodic time-series tasks. Our method builds upon the well-known mixup technique by incorporating a novel approach. We evaluate our proposed method on three time-series tasks, including heart rate estimation, human activity recognition, and cardiovascular disease detection.
• Score: 26.053496478247236
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The success of contrastive learning is well known to be dependent on data augmentation. Although the degree of data augmentations has been well controlled by utilizing pre-defined techniques in some domains like vision, time-series data augmentation is less explored and remains a challenging problem due to the complexity of the data generation mechanism, such as the intricate mechanism involved in the cardiovascular system. Moreover, there is no widely recognized and general time-series augmentation method that can be applied across different tasks. In this paper, we propose a novel data augmentation method for quasi-periodic time-series tasks that aims to connect intra-class samples together, and thereby find order in the latent space. Our method builds upon the well-known mixup technique by incorporating a novel approach that accounts for the periodic nature of non-stationary time-series. Also, by controlling the degree of chaos created by data augmentation, our method leads to improved feature representations and performance on downstream tasks. We evaluate our proposed method on three time-series tasks, including heart rate estimation, human activity recognition, and cardiovascular disease detection. Extensive experiments against state-of-the-art methods show that the proposed approach outperforms prior works on optimal data generation and known data augmentation techniques in the three tasks, reflecting the effectiveness of the presented method. Source code: https://github.com/eth-siplab/Finding_Order_in_Chaos

Related papers

• An Unsupervised Approach for Periodic Source Detection in Time Series [22.053675805215686]
  Detection of periodic patterns of interest within noisy time series data plays a critical role in various tasks. Existing learning techniques often rely on labels or clean versions of signals for detecting the periodicity. We propose a novel method to detect the periodicity in time series without the need for any labels.
  arXiv  Detail & Related papers  (2024-06-01T22:23:51Z)
• Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series [49.992908221544624]
  Time series data often exhibit numerous missing values, which is the time series imputation task. Previous deep learning methods have been shown to be effective for time series imputation. We propose a non-generative time series imputation method that produces accurate imputations with inherent uncertainty.
  arXiv  Detail & Related papers  (2023-12-03T05:52:30Z)
• Time Series Contrastive Learning with Information-Aware Augmentations [57.45139904366001]
  A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples. How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question. We propose a new contrastive learning approach with information-aware augmentations, InfoTS, that adaptively selects optimal augmentations for time series representation learning.
  arXiv  Detail & Related papers  (2023-03-21T15:02:50Z)
• LEAVES: Learning Views for Time-Series Data in Contrastive Learning [16.84326709739788]
  We propose a module for automating view generation for time-series data in contrastive learning, named learning views for time-series data (LEAVES) The proposed method is more effective in finding reasonable views and performs downstream tasks better than the baselines.
  arXiv  Detail & Related papers  (2022-10-13T20:18:22Z)
• Automatic Data Augmentation via Invariance-Constrained Learning [94.27081585149836]
  Underlying data structures are often exploited to improve the solution of learning tasks. Data augmentation induces these symmetries during training by applying multiple transformations to the input data. This work tackles these issues by automatically adapting the data augmentation while solving the learning task.
  arXiv  Detail & Related papers  (2022-09-29T18:11:01Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Contrastive Neural Processes for Self-Supervised Learning [1.8059331230167266]
  We propose a novel self-supervised learning framework that combines contrastive learning with neural processes. It relies on recent advances in neural processes to perform time series forecasting. Unlike previous self-supervised methods, our augmentation pipeline is task-agnostic, enabling our method to perform well across various applications.
  arXiv  Detail & Related papers  (2021-10-24T21:01:27Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Time Series Data Augmentation for Deep Learning: A Survey [35.2161833151567]
  We systematically review different data augmentation methods for time series data. We empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting.
  arXiv  Detail & Related papers  (2020-02-27T23:38:11Z)
• Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation [57.78765460295249]
  We develop a novel automatic learning-based data augmentation method for medical image segmentation. In our method, we innovatively combine the data augmentation module and the subsequent segmentation module in an end-to-end training manner with a consistent loss. We extensively evaluated our method on CT kidney tumor segmentation which validated the promising results of our method.
  arXiv  Detail & Related papers  (2020-02-22T14:10:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.