Adaptive machine learning for pattern extraction and network analysis in time-series data

自适应机器学习用于时间序列数据的模式提取和网络分析本博士研究将侧重于理论机器学习，并将开发网络分析模型来分析时间序列数据模式中的相关性和因果关系。这项工作将首先发展概率和信息理论驱动的方法（如贝叶斯网络和基于熵的模型）。本研究也将探索发展动态网路学习模型及因果推理分析，以处理多元时间序列资料的模式。

Adaptive machine learning for pattern extraction and network analysis in time-series data This PhD research will focus ontheoretical machine learning and will develop network analysis models to analyse the correlation and causal relations intime-series data patterns. The work will initially develop probabilistically and information theory-driven methods (such asBayesian networks and entropy-based models). The research will also explore developing dynamic network learningmodels and causal inference analysis in processing the patterns in multivariate time-series data.