MTS-LSTDM: Multi-Time-Scale Long Short-Term Double Memory for power load forecasting

Lou, Yiwei; Huang, Yu; Xing, Xuliang; Cao, Yongzhi; Wang, Hanpin

doi:10.1016/j.sysarc.2022.102443

Cited by 11 publications

(1 citation statement)

References 34 publications

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“…Synthetic data has demonstrated robust results in overcoming data limitations for various tasks such as dataset balancing (Xiao, Wu, and Lin 2021), data analysis (Cortés et al 2022;Zhang et al 2019;Lou et al 2022) and privacy preservation (Faisal et al 2022;Liu et al 2022). Nowadays, the main popular methods to synthesize tabular data by using deep learning are based on GANs.…”

Section: Related Work Tabular Data Synthesismentioning

confidence: 99%

A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis

Zhang,

Lou,

et al. 2024

AAAI

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The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realistic multimodal tabular data for scientific research. Our approach introduces a transformer-based fusion module that seamlessly integrates multimodal features, permitting for mining more informative latent representations. The attention within the fusion module directs the integrated output features to focus on critical components that facilitate the task of generating latent embeddings. Moreover, we formulate a contrastive learning strategy to implicitly constrain the embeddings from discrete features in the latent feature space by encouraging the similar discrete feature distributions closer while pushing the dissimilar further away, in order to better enhance the representation of the latent embedding. Experimental results indicate that GTCoder is effective to generate photo-realistic synthetic data, with interactive interpretation of latent embedding, and performs favorably against some baselines on most real-world and simulated datasets.

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