# 时间序列分析代写Time Series Analysis代考

## 代写时间序列分析作业代写Time Series Analysis

### 探索性分析 Exploratory analysis代写

• Function approximation函数近似
• Prediction and forecasting预测和预报
• Signal estimation信号估计

## 经济学中的统计方法的相关

Time Series Analysis is the way of studying the characteristics of the response variable with respect to time, as the independent variable. To estimate the target variable in the name of predicting or forecasting, use the time variable as the point of reference. In this article we will discuss in detail TSA Objectives, Assumptions, Components (stationary, and Non- stationary). Along with the TSA algorithm and specific use cases in Python.

## 时间序列分析相关课后作业代写

$$\begin{gathered} x_{1, t} \simeq-(1-\theta B)^{-1} a_{t-1}=-\sum_{j=0}^{\infty} \theta^{j} B^{j} a_{t-1} \ x_{2, t} \simeq-\left(1-\Theta B^{12}\right)^{-1} a_{t-12}=-\sum_{i=0}^{\infty} \Theta^{i} B^{12 i} a_{t-12} \end{gathered}$$
Therefore, for large samples, the information matrix is
$$\mathbf{I}(\theta, \Theta)=n\left[\begin{array}{ll} \left(1-\theta^{2}\right)^{-1} & \theta^{11}\left(1-\theta^{12} \Theta\right)^{-1} \ \theta^{11}\left(1-\theta^{12} \Theta\right)^{-1} & \left(1-\Theta^{2}\right)^{-1} \end{array}\right]$$