对于不完整的时间序列建模的时间感知神经普通微分方程
Zhuoqing Chang1, Shubo Liu1, Run Qiu1
1School of Computer Science, Wuhan University, 299# Bayi Rd, Wuchang District, Wuhan, 430072 Hubei China.
The Journal of supercomputing
|June 26, 2023
概括
本研究引入了时间意识的神经普通微分方程 (TN-ODE) 来建模不完整的时间序列数据. TN-ODE有效地赋值缺失值,并执行多步预测,优于现有方法.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 产生了大量的时间序列数据.
- 现实世界时间序列数据经常包含由于传感器问题或噪声而缺失的值.
- 当前处理缺失时间序列数据的方法往往会降低时间信息并引入错误.
研究的目的:
- 提出一种新的连续神经网络架构,时间意识的神经普通微分方程 (TN-ODE),用于建模不完整的时间序列数据.
- 为了在任何时间点准确地归算缺失的值.
- 为了方便在特定的未来时间点进行多步预测.
主要方法:
- 开发了一种连续神经网络架构TN-ODE.
- 利用一个时间意识的长短期记忆 (LSTM) 作为一个编码器,从部分观测中学习.
- 用连续时间动态的完全连接网络参数化潜态的导数.
主要成果:
- 与基线方法相比,TN-ODE在数据插入和抽取任务中表现优异.
- 该模型在归算和预测方面实现了较低的平均平方误差 (MSE).
- 此外,TN-ODE在下游时间序列分类任务中也显示出更高的准确性.
结论:
- 在不影响时间信息的情况下,TN-ODE提供了一个有效的解决方案来建模不完整的时间序列数据.
- 该架构成功地处理了缺失值赋值和多步预测.
- TN-ODE代表了物联网应用时间序列分析的重大进步.
相关概念视频
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01:31Classification of Systems-II
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01:27State Space Representation
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01:23Linear time-invariant Systems
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01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
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