统计代写|MATH743 Time-Series Analysis
Statistics-lab™可以为您提供unh.edu MATH743 Time-Series Analysis时间序列分析的代写代考和辅导服务！
MATH743 Time-Series Analysis课程简介
Time series data are intriguing yet complicated information to work with. While this
Credits 3 course will provide students with a basic understanding of the nature and basic processes used to analyze such data, you will quickly realize that this is a small first step in being able to confidently understand what trends might exist within a set of data and the complexities of being able to use this information to make predictions or forecasts. Yet, whether it is financial, medical or weather related, this type of data is quite frequently found in much of our daily lives.
PREREQUISITES
Topics typically covered in this graduate level course include:
- Understanding the characteristics of time series data
- Understanding moving average models and partial autocorrelation as foundations for analysis of time series data
- Exploratory Data Analysis – Trends in time series data
- Using smoothing and removing trends when working with time series data
- Understanding how periodograms are used with time series data
- Implementing ARMA and ARIMA time series models
- Identifying and interpreting various patterns for intervention effects
- Examining the analysis of repeated measures design
- Using ARCH and AR models in multivariate time series contexts
- Using spectral density estimation and spectral analysis
- Using fractional differencing and threshold models with time series data
MATH743 Time-Series Analysis HELP（EXAM HELP， ONLINE TUTOR）
2.5 Find the command for graphing quarterly seasonal plots in the Python package statsmodels, and then graph the seasonal plots for the dataset in Example 2.6 as well as give your comment on the plot.
2.6 Use the multiplicative Holt-Winters smoothing method to analyze the dataset “gdpquarterlychina1992.1-2017.4.csv,” and compare your results with the results in Example 2.6
2.7 The time series data “AustraliaUnemployedTotalPersons” in the folder “Ptsadata” is from the Australian Bureau of Statistics. Make an exploratory data analysis of it.
2.8 For the interpretation of economic statistics such as unemployment data or GDP data, it is important to identify the presence of seasonal components and to remove them so as to eliminate the effect of seasonal influence on the raw time series and reveal the true trend. This process is called the seasonal adjustment, and if the seasonal components are removed from the original time series data, the resulting values are known as the seasonally adjusted time series. Using the seasonal decompose function, make a seasonal adjustment of the time series data “gdpquarterlychina1992.1-2017.4.csv,” and describe what is the difference between the seasonally adjusted series and the trend component.
Textbooks
• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.
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