brockwell 时间序列的理论与方法和Introduction to time series and forecasting 免费中文版和英文版，时间序列经典教材，对数学要求较高。看到有人高价出售，表示有点看不过去，现免费给大家阅读。PS:这里吐槽一下中文版的翻译者，田老师怎么说也算前辈，不敢妄加指责，但是这位老先生翻译的时间序列出了很多错误，而且有一部分是在相当重要的结论性的式子上，浪费我好多不必要的时间，所以，小的在这里还是推荐各位有较高英语阅读能力的同仁直接去看英文原版。
目录 第一章平稳时间序列
1.1时间序列实例
1.2随机过程
1.3平稳和严平稳
1.4趋势项和季节项的估计和分离
1.5平稳过程的自协方差函数
1.6多元正态分布
1.7Kolmogorov定理的应用
习题

第二章Hilbert空间
2.1内积空间及其性质
2.2Hilbert空间
2.3投影定理
2.4正交集
2.5R中的投影
2.6线性回归和一般线性模型
2.7均方收敛，条件期望和最佳线性预报
2.8Fourier级数
2.9Hilbert空间的同构
2.10L2的完备性
2.11Fourier级数的补充知识
习题二

第三章平稳AMAR过程
3.1因果可逆ARMA过程
3.2无限阶滑动平均过程
3.3ARMA（p，q）过程自协方差函数的计算
3.4偏自相关（系数）函数
3.5自协方差母函数
3.6常系数线性齐次差分方程
习题三

第四章平稳过程的谱表示
4.1复值平稳时间序列
4.2正弦函数线性组合的谱分布
4.3Herglotz定理
4.4谱密度与ARMA过程
4.5循环行列式与其特征值
4.6[一兀，兀]上的正交增量过程
4.7关于正交增量过程的积分
4.8谱表示
4.9反演公式
4.10时不变线性滤波器
4.11逼近的性质
习题四

第五章平稳过程的预报
5.1时域中的预报方程
5.2最佳线性预报的递推计算方法
5.3ARMA（p，q）过程的递归预报
5.4平稳Gauss过程的预报；预报界
5.5因果可逆ARMA过程基于表示的预报
5.6频域中的预报
5.7Wold分解
5.8Kolrnogorov公式
习题五

第六章渐近理论
6.1依概率收敛
6.2阶收敛（r>0）
6.3依分布收敛
6.4中心极限定理和有关结论
习题六

第七章均值和自协方差函数的估计
7.1u的估计
7.2R（·）和p（·）的估计
7.3渐近分布的推论
习题七

第八章ARMA模型.的估计
8.1自回归过程的Yule-Walker方程和参数估计
8.2应用Durbin-Levinson算法的自回归过程初估计
8.3滑动平均过程参数的新息估计
8.4ARMA（p，q）过程的初估计
8.5关于渐近有效性的附注
8.6任意零均值Gauss过程的似然函数的递归计算
8.7ARMA过程的极大似然函数和最小二乘估计
8.8极大似然估计的渐近性质
8.9因果可逆ARMA过程参数的置信区间
8.10Yule-Walker估计的渐近性质
8.11参数估计的渐近正态性
习题八

第九章利用ARIMA过程建模和预报
9.1非平稳时间序列的ARIMA模型
9.2辨识方法
9.3AIC准则
9.4诊断检验
9.5ARIMA过程预报
9.6季节ARIMA模型
习题九

第十章平稳过程的谱推断
10.1周期图
10.2隐含周期的存在性检验
10.3周期图的渐近性质
10.4平滑周期图
10.5关于谱的置信区间
10.6自回归谱估计、极大熵谱估计、滑动平均谱估计和极大似然ARMA谱估计
10.7快速Fourier变换算法
10.8ARMA模型系数的最小二乘估计与极大似然估计渐近性的证明
习题十

第十一章多维时间序列
11.1多维时间序列的二阶性质
11.2均值和协方差函数的估计
11.3多维ARMA过程
11.4二阶矩随机向量的最佳线性预报
11.5关于多维.ARMA过程的估计
11.6互谱
11.7互谱的估计
11.8多维平稳时间序列的谱表示
习题十

第十二章状态－空间模型和Kalman递归式
12.1状态－空间模型
12.2Kalman递归式
12.3带有缺失观测值的状态－空间模型
12.4可控制性和可观测性
12.5递归Bayes状态估计
习题十二

第十三章进一步的专题
13.1传递函数建模
13.2长记忆过程
13.3具有无限方差的线性过程
13.4门限模型
习题十三
附录数据集
中英文词汇对照


1 INTRODUCTION
1.1 Examples of Time Series
1.2 Objectives of Time Series Analysis
1.3 Some Simple Time Series Models
1.3.3 A General Approach to Time Series Modelling
1.4 Stationary Models and the Autocorrelation Function
1.4.1 The Sample Autocorrelation Function
1.4.2 A Model for the Lake Huron Data
1.5 Estimation and Elimination of Trend and Seasonal Components
1.5.1 Estimation and Elimination of Trend in the Absence of Seasonality
1.5.2 Estimation and Elimination of Both Trend and Seasonality
1.6 Testing the Estimated Noise Sequence
1.7 Problems
2 STATIONARY PROCESSES
2.1 Basic Properties
2.2 Linear Processes
2.3 Introduction to ARMA Processes
2.4 Properties of the Sample Mean and Autocorrelation Function
2.4.2 Estimation of $\gamma(\cdot)$ and $\rho(\cdot)$
2.5 Forecasting Stationary Time Series
2.5.3 Prediction of a Stationary Process in Terms of Infinitely Many Past Values
2.6 The Wold Decomposition
1.7 Problems
3 ARMA MODELS
3.1 ARMA($p,q$) Processes
3.2 The ACF and PACF of an ARMA$(p,q)$ Process
3.2.1 Calculation of the ACVF
3.2.2 The Autocorrelation Function
3.2.3 The Partial Autocorrelation Function
3.3 Forecasting ARMA Processes
1.7 Problems
4 SPECTRAL ANALYSIS
4.1 Spectral Densities
4.2 The Periodogram
4.3 Time-Invariant Linear Filters
4.4 The Spectral Density of an ARMA Process
1.7 Problems
5 MODELLING AND PREDICTION WITH ARMA PROCESSES
5.1 Preliminary Estimation
5.1.1 Yule-Walker Estimation
5.1.3 The Innovations Algorithm
5.1.4 The Hannan-Rissanen Algorithm
5.2 Maximum Likelihood Estimation
5.3 Diagnostic Checking
5.3.1 The Graph of $\t=1,\ldots,n\
5.3.2 The Sample ACF of the Residuals
5.3.3 Tests for Randomness of the Residuals
5.4 Forecasting
5.5 Order Selection
1.7 Problems
6 NONSTATIONARY AND SEASONAL TIME SERIES
6.1 ARIMA Models for Nonstationary Time Series
6.2 Identification Techniques
6.3 Unit Roots in Time Series Models
6.3.1 Unit Roots in Autoregressions
6.3.2 Unit Roots in Moving Averages
6.4 Forecasting ARIMA Models
6.5 Seasonal ARIMA Models
6.5.1 Forecasting SARIMA Processes
6.6 Regression with ARMA Errors
1.7 Problems
7 MULTIVARIATE TIME SERIES
7.1 Examples
7.2 Second-Order Properties of Multivariate Time Series
7.3 Estimation of the Mean and Covariance Function
7.3.2 Estimation of $\Gamma(h)$
7.3.3 Testing for Independence of Two Stationary Time Series
7.4 Multivariate ARMA Processes
7.4.1 The Covariance Matrix Function of a Causal ARMA Process
7.5 Best Linear Predictors of Second-Order Random Vectors
7.6 Modelling and Forecasting with Multivariate AR Processes
7.6.1 Estimation for Autoregressive Processes Using Whittle's Algorithm
7.6.2 Forecasting Multivariate Autoregressive Processes
7.7 Cointegration
1.7 Problems
8 STATE-SPACE MODELS
8.1 State-Space Representations
8.2 The Basic Structural Model
8.3 State-Space Representation of ARIMA Models
8.4 The Kalman Recursions
8.5 Estimation for State-Space Models
8.6 State-Space Models with Missing Observations
8.7 The EM Algorithm
8.8 Generalized State-Space Models
1.7 Problems
9 FORECASTING TECHNIQUES
9.1 The ARAR Algorithm
9.1.1Memory Shortening
9.1.2Fitting a Subset Autoregression
9.1.3Forecasting
9.1.4Running the Program ARAR
9.2 The Holt-Winters Algorithm
9.3 The Holt-Winters Seasonal Algorithm
9.4 Choosing a Forecasting Algorithm
1.7 Problems
10 FURTHER TOPICS
10.1 Transfer Function Models
10.1.1 Prediction Based on a Transfer-Function Model
10.2 Intervention Analysis
10.3 Nonlinear Models
10.3.1 Deviations From Linearity
10.3.2 Chaotic Deterministic Sequences
10.3.3 Distinguishing Between White Noise and IID Sequences
10.3.4 Three Useful Classes of Nonlinear Models
10.4 Continuous-Time Models
10.5 Long-Memory Models
10.4 ProblemsAPPENDIX
Appendix A Random Variables
A.1 Distribution Functions and Expectation
A.2 Random Vectors
A.3 The Multivariate Normal Distribution
A.3 Problems
Appendix B Statistical Complements
B.1 Least Squares Estimation
B.1.1 The Gauss-Markov Theorem
B.1.2 Generalized Least Squares
B.2 Maximum Likelihood Estimation
B.2.1 Properties of Maximum Likelihood Estimators
B.3 Confidence Intervals
B.3.1 Large-Sample Confidence Regions
B.4 Hypothesis Testing
B.4.2 Large-Sample Tests Based on Confidence Regions
Appendix C Mean Square Convergence
C.1 The Cauchy Criterion
Appendix D An ITSM Tutorial
D.1 Getting Started
D.2 Preparing Your Data for Modelling
D.3 Finding a Model for Your Data
D.4 Testing Your Model
D.4.3 Testing for Randomness of the Residuals
D.5 Prediction
D.6 Model Properties
D.6.4 Generating Realizations of a Random Series
Bibliography
Index
两本书的pdf格式文件：
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