Machine Learning of Time Series Using Time-Delay Embedding and Precision Annealing

Ty Alexander J. A.; Fang Zheng; Gonzalez Rivver A.; Rozdeba Paul J.; Abarbanel Henry D. I

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Machine Learning of Time Series Using Time-Delay Embedding and Precision Annealing

机译：使用时间延迟嵌入和精确退火的时间序列机器学习

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Tasking machine learning to predict segments of a time series requires estimating the parameters of a ML model with input/output pairs from the time series. We borrow two techniques used in statistical data assimilation in order to accomplish this task: time-delay embedding to prepare our input data and precision annealing as a training method. The precision annealing approach identifies the global minimum of the action (-log[P]). In this way, we are able to identify the number of training pairs required to produce good generalizations (predictions) for the time series. We proceed from a scalar time series s(tn);tn=t0+n Delta t and, using methods of nonlinear time series analysis, show how to produce a DE>1-dimensional time-delay embedding space in which the time series has no false neighbors as does the observed s(tn) time series. In that DE-dimensional space, we explore the use of feedforward multilayer perceptrons as network models operating on DE-dimensional input and producing DE-dimensional outputs.

机译：对机器学习进行任务分配以预测时间序列的分段需要使用时间序列中的输入/输出对来估计ML模型的参数。为了完成此任务，我们借用了两种用于统计数据同化的技术：延时嵌入以准备我们的输入数据；精确退火作为一种训练方法。精确退火方法确定了动作的全局最小值（-log [P]）。通过这种方式，我们能够确定为时间序列产生良好的概括（预测）所需的训练对的数量。我们从标量时间序列s（tn）; tn = t0 + n Delta t开始，并使用非线性时间序列分析的方法，展示了如何产生DE>一维时间延迟嵌入空间，其中时间序列具有观察到的s（tn）时间序列没有虚假邻居。在该DE维空间中，我们探索使用前馈多层感知器作为在DE维输入上运行并产生DE维输出的网络模型。

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来源
《Neural computation》 |2019年第10期|2004-2024|共21页
作者
Ty Alexander J. A.; Fang Zheng; Gonzalez Rivver A.; Rozdeba Paul J.; Abarbanel Henry D. I;
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作者单位

Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA;

Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA;

Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA;

Univ Potsdam, Inst Math, D-14476 Potsdam, Germany;

Univ Calif San Diego, Scripps Inst Oceanog, Dept Phys, La Jolla, CA 92093 USA|Univ Calif San Diego, Scripps Inst Oceanog, Marine Phys Lab, La Jolla, CA 92093 USA;

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收录信息美国《科学引文索引》(SCI);美国《化学文摘》(CA);
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正文语种 eng
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专利

1. Machine Learning of Time Series Using Time-Delay Embedding and Precision Annealing [J] . Ty Alexander J. A., Fang Zheng, Gonzalez Rivver A., Neural computation . 2019,第10期

机译：使用时间延迟嵌入和精密退火的时间序列机器学习
2. Nonlinear time series forecasting of time-delay neural network embedded with Bayesian regularization [J] . Jiang WJ, Zhang LM, Wang P Applied mathematics and computation . 2008,第1期

机译：嵌入贝叶斯正则化的时延神经网络的非线性时间序列预测
3. Effect of Sparse Representation of Time Series Data on Learning Rate of Time-Delay Neural Networks [J] . Kalantari Khandani Masoumeh, Mikhael Wasfy B. Circuits, systems and signal processing . 2021,第6期

机译：时间序列数据稀疏表示对时滞神经网络学习率的影响
4. Research on Nonlinear Time Series Forecasting of Time-Delay NN Embedded with Bayesian Regularization [C] . Weijin Jiang, Yusheng Xu, Yuhui Xu, Artificial intelligence and computational intelligence . 2009

机译：贝叶斯正则化嵌入时延神经网络的非线性时间序列预测研究
5. Machine Learning Model Time-Series Clustering for Energy Optimized Virtual Machine Placement Using Time-Series Performance Data [D] . Kellogg, Tad. 2021

机译：机器学习模型时间序列聚类用于使用时间序列性能数据进行能量优化虚拟机的展示
6. A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series [O] . Huihui Qiao, Taiyong Wang, Peng Wang, 2018

机译：多传感器时间序列的机器健康监测的时空分布时空特征学习方法
7. Machine Learning of Time Series Using Time-Delay Embedding and Precision Annealing [O] . Alexander J. A. Ty, Zheng Fang, Rivver A. Gonzalez, 2019

机译：使用时间延迟嵌入和精密退火的时间序列机器学习
8. Error Statistics of Time-Delay Embedding Prediction on Chaotic Time Series [R] . Wood, J. T. 1999

机译：混沌时间序列的时延嵌入预测误差统计

Machine Learning of Time Series Using Time-Delay Embedding and Precision Annealing

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