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<p>Generative adversarial networks have achieved strong results in computer vision, but their use in time series forecasting remains limited. This paper proposes a conditional noise generative adversarial network with a Siamese neural network as discriminator for long-term forecasting. The method combines the simplicity of a linear model with a generative framework, introducing a triplet margin loss to capture relationships between samples and conditional noise to improve sample generation. Experiments on eight open-source datasets show an average improvement of 8.42 percent, and a 192.8 percent gain for longer-term forecasting, with further improvement on a real-world telecommunications dataset.</p>

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Conditional noise generative adversarial networks with Siamese neural network for longer time series forecasting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Haotian Mao,
  • Xiao Feng

摘要

Generative adversarial networks have achieved strong results in computer vision, but their use in time series forecasting remains limited. This paper proposes a conditional noise generative adversarial network with a Siamese neural network as discriminator for long-term forecasting. The method combines the simplicity of a linear model with a generative framework, introducing a triplet margin loss to capture relationships between samples and conditional noise to improve sample generation. Experiments on eight open-source datasets show an average improvement of 8.42 percent, and a 192.8 percent gain for longer-term forecasting, with further improvement on a real-world telecommunications dataset.