Riemann surface
matlab

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以下是一些我们可以帮助您解决的问题:

时间序列分析基础概念:涵盖时间序列分析的基本概念,如随机过程、平稳性和自相关等。

单变量时间序列模型:研究单变量时间序列模型的建立和估计,包括AR、MA、ARMA和ARIMA模型等。

时间序列的预测:介绍时间序列预测的方法和技术,包括移动平均法和指数平滑法等。

多元时间序列分析:探索多元时间序列模型的性质和估计方法,如VAR模型。

频域分析:研究时间序列的频域分析方法,如傅里叶变换和谱密度分析。

非线性时间序列模型:介绍非线性时间序列模型的建立和估计,如GARCH模型和非线性自回归模型。

状态空间模型和卡尔曼滤波:探讨状态空间模型的概念以及卡尔曼滤波在时间序列分析中的应用。

无论您在时间序列分析方面遇到的问题是什么,我们都将尽全力为您提供专业的帮助,确保您的学习之旅顺利无阻!

问题 1.

EXERCISES
1.1 Use software to produce the time series plot shown in Exhibit 1.2, on page 2. The data are in the file named larain. ${ }^{\dot{1}}$
1.2 Produce the time series plot displayed in Exhibit 1.3, on page 3. The data file is named color.
1.3 Simulate a completely random process of length 48 with independent, normal values. Plot the time series plot. Does it look “random”? Repeat this exercise several times with a new simulation each time.
1.4 Simulate a completely random process of length 48 with independent, chi-square distributed values, each with 2 degrees of freedom. Display the time series plot. Does it look “random” and nonnormal? Repeat this exercise several times with a new simulation each time.
1.5 Simulate a completely random process of length 48 with independent, $t$-distributed values each with 5 degrees of freedom. Construct the time series plot. Does it look “random” and nonnormal? Repeat this exercise several times with a new simulation each time.
1.6 Construct a time series plot with monthly plotting symbols for the Dubuque temperature series as in Exhibit 1.7, on page 6. The data are in the file named tempdub.

问题 2.

EXERCISES
2.1 Suppose $E(X)=2, \operatorname{Var}(X)=9, E(Y)=0, \operatorname{Var}(Y)=4$, and $\operatorname{Corr}(X, Y)=0.25$. Find:
(a) $\operatorname{Var}(X+Y)$.
(b) $\operatorname{Cov}(X, X+Y)$.
(c) $\operatorname{Corr}(X+Y, X-Y)$.
2.2 If $X$ and $Y$ are dependent but $\operatorname{Var}(X)=\operatorname{Var}(Y)$, find $\operatorname{Cov}(X+Y, X-Y)$.
2.3 Let $X$ have a distribution with mean $\mu$ and variance $\sigma^2$, and let $Y_t=X$ for all $t$.
(a) Show that $\left{Y_t\right}$ is strictly and weakly stationary.
(b) Find the autocovariance function for $\left{Y_t\right}$.
(c) Sketch a “typical” time plot of $Y_t$.

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