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以数据为基础的储库计算,用于高效的时间序列预测
Felix Köster1, Dhruvit Patel2, Alexander Wikner2
1Institut for Theoretical Physics, Technische Universität Berlin, 10623 Berlin, Germany.
Chaos (Woodbury, N.Y.)
|July 6, 2023
概括
我们介绍了基于数据的储计算 (DI-RC),这是一种新的预测方法,可以提高准确性并降低计算成本. 这种数据驱动的方法提高了时间序列预测,即使没有广泛的参数调整.
科学领域:
- 计算科学 计算科学
- 应用数学 应用数学 应用数学
- 机器学习 机器学习
背景情况:
- 动态系统预测对于理解复杂现象至关重要.
- 传统方法通常需要广泛的超参数优化或依赖于不可用的基于知识的模型.
- 储库计算 (RC) 提供了一个强大的机器学习框架,但可以是计算密集型和对参数选择敏感的.
研究的目的:
- 为动态系统预测提出和评估一种新的数据知情储库计算 (DI-RC) 方法.
- 与现有方法相比,提高预测准确度和降低计算成本.
- 为了减轻储库计算中繁的超参数优化需求.
主要方法:
- 开发了一种混合方法,将数据驱动的模型发现技术与基于延迟的储计算机 (RC) 结合起来.
- 用于数据驱动组件的非线性动态系统 (SINDy) 的稀疏识别.
- 在Lorenz和Kuramoto-Sivashinsky系统上测试了DI-RC方法.
主要成果:
- 与单个组件方法相比,DI-RC证明了时间序列预测准确度的提高.
- 该方法显著降低了计算成本.
- 当储参数未经优化时,性能增长最明显,突出显示了超参数灵敏度降低.
结论:
- 基于数据的储计算 (DI-RC) 为动态系统预测提供了一个计算效率高,准确的替代方案.
- 当基于知识的模型无法使用时,这种方法特别有价值.
- DI-RC成功地将数据驱动模型发现与机器学习相结合,以实现可靠的预测.
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