基于适应变压器神经网络和基于FFT的特征提取的时间序列预测模型
Kyrylo Yemets1, Ivan Izonin1,2, Ivanna Dronyuk3
1Department of Artificial Intelligence, Lviv Polytechnic National University, 79905 Lviv, Ukraine.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究引入了用于时间序列预测的增强型变压器模型,通过使用快速里埃变换 (FFT) 来增加频域特征来提高准确性. 这种新的方法显著提高了传感器数据的预测性能.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 准确的时间序列预测在金融,气候学和工程学中至关重要.
- 神经网络因体积,噪声和长期依赖而难以处理传感器数据.
- 现有的模型面临着各种传感器数据特征的挑战.
研究的目的:
- 为了提高传感器收集数据的时间序列预测准确度.
- 解决当前神经网络模型在处理复杂时间序列方面的局限性.
- 通过结合频域信息来提高预测性能.
主要方法:
- 为时间序列预测提出了一个适应的变压器架构.
- 引入了一种使用快速富里埃转换 (FFT) 来将时间域转换为频率域的数据预处理方法.
- 用复杂值的频域特征丰富数据,以增强信息内容.
主要成果:
- 拟议的模型在三个不同的传感器数据集中展示了卓越的性能.
- 与LSTM,DeepAR和Transformer等最先进的模型相比,实现了更高的准确性.
- 在五个不同的绩效指标上始终优于现有方法.
结论:
- 适应的变压器模型与FFT预处理显著提高时间序列预测的准确性.
- 该方法有效地应对大量,噪音和对传感器数据的长期依赖所带来的挑战.
- 这种方法为数据驱动应用程序的准确预测提供了强大的解决方案.
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