大型多元时间序列数据的基于滑动窗口的多阶段聚类和概率预测方法

Temporal Pattern Attention for Multivariate Time Series Forecasting

TensorFlow版本：1.9.0 Keras版本：2.0.2 我的博客： :

蔡瑞胸的<>中文版，带书签

一种基于CNN模型多元时间序列分类结构，佘强，李静林，多元时间序列分类问题是时间序列挖掘领域中的重要问题，目前的常规做法是使用基于欧氏距离或 DTW 距离的K近邻分类模型，或基于统计

多元时间序列的因果关系分析是数据挖掘领域的研究热点. 时间序列数据包含着与时间动态有关的、未知的、有价值的信息, 因此若能挖掘出这些知识进而对时间序列未来趋势进行预测或干预, 具有重要的现实意义.

华章数学译丛59 多元时间序列分析及金融应用 R语言 中英文

Multivariate time series analysis considers simultaneously multiple time series. It is a branch of multivariate statistical analysis but deals specifically with dependent data. It is, in general, much more complicated than the univariate time series analysis, especially when the number of series considered is large. We study this more complicated statistical analysis in this book because in real life decisions often involve multiple inter-related factors or variables. Understanding the relationships between those factors and providing accurate predictions of those variables are valuable in decision making. The objectives of multivariate time series analysis thus include 1. To study the dynamic relationships between variables 2. To improve the accuracy of prediction