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Conditional noise generative adversarial networks with Siamese neural network for longer time series forecasting
1China Telecom Corporation Limited Jiangsu Branch, Nanjing, 210000, China. maohaotiancs@outlook.com.
Scientific Reports
|December 2, 2025
Summary
This study introduces a novel generative adversarial network for time series forecasting, significantly improving long-term prediction accuracy. The enhanced model shows substantial gains on multiple datasets, particularly for extended forecasting horizons.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Time Series Analysis
Background:
- Generative adversarial networks (GANs) show promise in computer vision but have limited application in time series forecasting.
- Existing methods struggle with long-term time series prediction accuracy.
Purpose of the Study:
- To propose a new GAN-based model for improved long-term time series forecasting.
- To enhance sample generation and capture inter-sample relationships in forecasting.
Main Methods:
- A conditional noise generative adversarial network (GAN) was developed.
- A Siamese neural network was employed as the discriminator.
- Triplet margin loss and conditional noise were introduced to the generative framework.
Main Results:
- The proposed method achieved an average improvement of 8.42% across eight open-source datasets.
- A significant 192.8% gain was observed for longer-term forecasting tasks.
- Performance improvements were also demonstrated on a real-world telecommunications dataset.
Conclusions:
- The novel GAN architecture effectively addresses limitations in long-term time series forecasting.
- The integration of triplet margin loss and conditional noise enhances predictive accuracy.
- This approach offers a promising direction for advanced time series prediction.
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