时间序列分析及应用：R语言（第2版） (美)Cryer J.D.and Chan K.S. 潘红宇等译

Time Series Shapelets A New Primitive for Data Mining shapelets论文2009

brockwell时间序列理论与方法第一版+第二版（中文版）

Hamilton-Time Series Analysis Hamilton-Time Series Analysis Hamilton-Time Series Analysis

论文提出了一种新的融合空间尺度特征的时空序列预测建模方法。

Selected solutions to introduction to time series from Prof. Brockwell

Practical Time Series Analysis Master Time Series Data Processing, Visualization, and Modeling using Python 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Time Series Analysis With Applications in R (Springer)

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.)