Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders

Wang, Dingsu; Yao, Yan; Qiu, Ruizhong; Zhu, Yada; Guan, Kaiyu; Margenot, Andrew J.; Tong, Hanghang

doi:10.1145/3580305.3599444

Cited by 12 publications

(3 citation statements)

References 25 publications

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“…The loss function for RNN-VAEs is similar to traditional VAEs, consisting of a reconstruction loss (L rec ) and a regularization term (L reg ) to encourage a predefined distribution in the latent space [64], [65], [66], [67], [68].…”

Section: ) Recurrent Variational Autoencoders (Rnn-vae)mentioning

confidence: 99%

“…The loss function for conditional generative models depends on the specific architecture and conditions used but typically involves both the reconstruction loss and a term related to the conditions used for generation [69], [70], [64], [71], [72], [73], [74], [75], [76], [77] (Table II).…”

Section: ) Conditional Generative Models: Conditional Generative Mode...mentioning

confidence: 99%

“…[69], [70], [64], [71], [72], [73], [74], [75], [76], [77] 2) Data Augmentation through Noise Addition: Data Augmentation through Noise Addition involves injecting controlled noise into the time series data to generate variations and enhance the training dataset. This approach can be represented as follows: Given an original time series X = [x 1 , x 2 , .…”

Section: B Sequence Modeling Techniquesmentioning

confidence: 99%

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Overview of Data Augmentation Techniques in Time Series Analysis

ANNAKI,

RAHMOUNE,

BOURHALEB

2024

IJACSA

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Time series data analysis is vital in numerous fields, driven by advancements in deep learning and machine learning. This paper presents a comprehensive overview of data augmentation techniques in time series analysis, with a specific focus on their applications within deep learning and machine learning. We commence with a systematic methodology for literature selection, curating 757 articles from prominent databases. Subsequent sections delve into various data augmentation techniques, encompassing traditional approaches like interpolation and advanced methods like Synthetic Data Generation, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). These techniques address complexities inherent in time series data. Moreover, we scrutinize limitations, including computational costs and overfitting risks. However, it's essential to note that our analysis does not end with limitations. We also comprehensively analyzed the advantages and applicability of the techniques under consideration. This holistic evaluation allows us to provide a balanced perspective. In summary, this overview illuminates data augmentation's role in time series analysis within deep and machine-learning contexts. It provides valuable insights for researchers and practitioners, advancing these fields and charting paths for future exploration.

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