Aimed at econometricians who have completed at least one course in time series modeling, Multiple Time Series Modeling Using the SAS VARMAX Procedure will teach you the time series analytical possibilities that SAS offers today. Estimations of model parameters are now performed in a split second. For this reason, working through the identifications phase to find the correct model is unnecessary. Instead, several competing models can be estimated, and their fit can be compared instantaneously. Consequently, for time series analysis, most of the Box and Jenkins analysis process for univariate series is now obsolete. The former days of looking at cross-correlations and pre-whitening are over, because distributed lag models are easily fitted by an automatic lag identification method. The same goes for bivariate and even multivariate models, for which PROC VARMAX models are automatically fitted. For these models, other interesting variations arise: Subjects like Granger causality testing, feedback, equilibrium, cointegration, and error correction are easily addressed by PROC VARMAX. One problem with multivariate modeling is that it includes many parameters, making parameterizations unstable. This instability can be compensated for by application of Bayesian methods, which are also incorporated in PROC VARMAX. Volatility modeling has now become a standard part of time series modeling, because of the popularity of GARCH models. Both univariate and multivariate GARCH models are supported by PROC VARMAX. This feature is especially interesting for financial analytics in which risk is a focus. This book teaches with examples. Readers who are analyzing a time series for the first time will find PROC VARMAX easy to use; readers who know more advanced theoretical time series models will discover that PROC VARMAX is a useful tool for advanced model building.

这是笔者2019.9下载的UCR数据集，其中有较多的时间序列数据，方便大家下载学习，特此分享给大家。(在介绍中,没有说不许传播,如有侵权,会立即删除.)

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

以前上传的代码估计下载的人多, 被设置成了50个积分, 这里重新上传个. 经过测试的, 能够放心使用!

Long Short Term Memory Networks for Anomaly Detection in Time Series - LSTM在时序数据中的应用

2014 KDD论文“Correlating Events with Time Series for Incident Diagnosis”的学习与讲解。

Analyzing Neural Time Series Data图书

Introduction to Time Series and Forecasting.pdf

Statistical Signal Processing - Detection, Estimation, and Time Series Analysis;[Louis Scharf];统计信号处理里面的经典书籍;