Data Augmentation for Multivariate Time Series Classification: An Experimental Study

• URL: http://arxiv.org/abs/2406.06518v1
• Date: Mon, 10 Jun 2024 17:58:02 GMT
• Title: Data Augmentation for Multivariate Time Series Classification: An Experimental Study
• Authors: Romain Ilbert, Thai V. Hoang, Zonghua Zhang,
• Abstract summary: Despite the limited size of these datasets, we achieved classification accuracy improvements in 10 out of 13 datasets using the Rocket and InceptionTime models. This highlights the essential role of sufficient data in training effective models, paralleling the advancements seen in computer vision.
• Score: 1.5390962520179197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our study investigates the impact of data augmentation on the performance of multivariate time series models, focusing on datasets from the UCR archive. Despite the limited size of these datasets, we achieved classification accuracy improvements in 10 out of 13 datasets using the Rocket and InceptionTime models. This highlights the essential role of sufficient data in training effective models, paralleling the advancements seen in computer vision. Our work delves into adapting and applying existing methods in innovative ways to the domain of multivariate time series classification. Our comprehensive exploration of these techniques sets a new standard for addressing data scarcity in time series analysis, emphasizing that diverse augmentation strategies are crucial for unlocking the potential of both traditional and deep learning models. Moreover, by meticulously analyzing and applying a variety of augmentation techniques, we demonstrate that strategic data enrichment can enhance model accuracy. This not only establishes a benchmark for future research in time series analysis but also underscores the importance of adopting varied augmentation approaches to improve model performance in the face of limited data availability.

Related papers

• TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting [59.702504386429126]
  TimeRAF is a Retrieval-Augmented Forecasting model that enhance zero-shot time series forecasting through retrieval-augmented techniques. TimeRAF employs an end-to-end learnable retriever to extract valuable information from the knowledge base.
  arXiv  Detail & Related papers  (2024-12-30T09:06:47Z)
• VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification [47.92529531621406]
  We propose a novel method, VSFormer, that incorporates both discriminative patterns (shape) and numerical information (value) In addition, we extract class-specific prior information derived from supervised information to enrich the positional encoding. Extensive experiments on all 30 UEA archived datasets demonstrate the superior performance of our method compared to SOTA models.
  arXiv  Detail & Related papers  (2024-12-21T07:31:22Z)
• Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo [34.40047933452929]
  We present a pilot study on using reinforcement learning (RL) for time series data augmentation. Our method, ReAugment, tackles three critical questions: which parts of the training set should be augmented, how the augmentation should be performed, and what advantages RL brings to the process.
  arXiv  Detail & Related papers  (2024-09-10T07:34:19Z)
• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Data-Centric Long-Tailed Image Recognition [49.90107582624604]
  Long-tail models exhibit a strong demand for high-quality data. Data-centric approaches aim to enhance both the quantity and quality of data to improve model performance. There is currently a lack of research into the underlying mechanisms explaining the effectiveness of information augmentation.
  arXiv  Detail & Related papers  (2023-11-03T06:34:37Z)
• Phased Data Augmentation for Training a Likelihood-Based Generative Model with Limited Data [0.0]
  Generative models excel in creating realistic images, yet their dependency on extensive datasets for training presents significant challenges. Current data-efficient methods largely focus on GAN architectures, leaving a gap in training other types of generative models. "phased data augmentation" is a novel technique that addresses this gap by optimizing training in limited data scenarios without altering the inherent data distribution.
  arXiv  Detail & Related papers  (2023-05-22T03:38:59Z)
• Data Augmentation techniques in time series domain: A survey and taxonomy [0.20971479389679332]
  Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. This work systematically reviews the current state-of-the-art in the area to provide an overview of all available algorithms. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.
  arXiv  Detail & Related papers  (2022-06-25T17:09:00Z)
• An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation [91.62129090006745]
  This paper studies the distribution shift problem from the perspective of pre-training and data augmentation. We provide the first comprehensive empirical study focusing on pre-training and data augmentation.
  arXiv  Detail & Related papers  (2022-05-25T13:04:53Z)
• 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)

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.