多变量时间序列信息瓶
Denis Ullmann1, Olga Taran1, Slava Voloshynovskiy1
1Faculty of Science, University of Geneva, CUI, 1227 Carouge, Switzerland.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
这项研究引入了一种新的深度学习方法,用于时间序列预测,将时间数据压缩成类似图像的表示. 多重时间序列信息瓶 (MTS-IB) 模型显示了不同数据集的效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 传统的时间序列 (TS) 和多个时间序列 (MTS) 预测模型通常使用不同的深度学习架构.
- 现有的时间维度建模方法包括分解,生物启发的神经网络和变压器模型.
- 信息瓶 (IB) 框架在 TS 和 MTS 分析中未得到充分利用.
研究的目的:
- 通过调整图像处理技术,为MTS预测提出一种新的深度学习方法.
- 为了研究压缩时间维度对MTS分析的有效性.
- 引入多个时间序列信息瓶 (MTS-IB) 模型.
主要方法:
- 使用部分卷积将时间序列数据编码为类似2D图像的表示.
- 利用图像扩展方面的进步来预测时间序列中未见的部分.
- 将MTS-IB模型应用于各种现实数据集,包括电力生产,道路交通和太阳能活动.
主要成果:
- 拟议的MTS-IB模型与传统的TS模型相比,显示出具有竞争力的性能.
- 该模型基于信息理论原则.
- 该方法可以适应超越时间和空间的更高维度数据.
结论:
- MTS-IB模型为MTS预测提供了一种有效和多功能方法.
- 压缩时间维度是MTS分析的一个有价值的策略.
- 该模型的适用性涵盖了各种领域,包括能源,运输和天体物理学.
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