Posted on Categories:Time Series, 数据科学代写, 时间序列, 统计代写, 统计代考

# 统计代写|时间序列分析代写Time-Series Analysis代考|ISYE6402 RELATED WORK

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## 统计代写|时间序列分析代写Time-Series Analysis代考|RELATED WORK

This section is dedicated to literature review and includes:

1. The evolution of time series forecasting models from traditional mathematical approaches to newer deep learning models.
2. Older research studies with machine learning models and statistical methods for forecasting the trends of infectious diseases.
3. Recent studies that have employed various forecasting models to predict the COVID-19 outbreak-related statistics with deaths and recoveries.

Time series forecasting is a domain in which prior measurements of a random variable are evaluated to create a model for capturing the basic trends and their connections. This model estimates the future values of the variable. This approach is particularly effective in cases: (1) when there is no explanatory model that can establish an accurate link between the prediction variable and other explanatory variables; and (2) when little to no information about the distribution or process is available. In the past few decades, a lot of research has been directed towards developing new time series forecasting models and improving existing ones (Papastefanopoulos et al., 2020).

## 统计代写|时间序列分析代写Time-Series Analysis代考|METHODS

This section describes the predictive machine and deep learning models.
The predictive machine models are: (1) support vector regression (SVR), (2) polynomial regression (PR) and (3) vector autoregression (VAR).

The deep learning models are: (1) recurrent neural network (RNN), (2) long short-term memory (LSTM) and (3) gated recurrent units (GRUs).
The details for each model are as follows:

classification tasks (Shah, 2020; Smola \& Schölkopf, 2004). Based on the same principles as SVM, the SVR method is used for working with continuous-valued data instead of classification tasks (Vapnik et al., 1997). The intuitive approach for SVR is that it takes input data, and in order to process them via linear function, it applies mapping of input data to high-dimensional feature vector space through inbuilt non-linear function (Chuang et al., 2002). In the recent surge of forecasting models, SVR has stood its ground and has been successfully deployed in various fields with outstanding performance, becoming a standard out-of-the-box method in machine learning frameworks (Zhang et al., 2011).

## 统计代写|时间序列分析代写Time-Series Analysis代考|RELATED WORK

1. 时间序列预测模型从传统数学方法到更新的深度学习模型的演变。
2. 较早的研究使用机器学习模型和统计方法来预测传染病的趋势。
3. 最近的研究采用各种预测模型来预测与 COVID-19 爆发相关的死亡和康复统计数据。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。