STA 6856 - Time Series Analysis

UWF - Mathematics and Statistics

Time series data is data that is focused on time and can be utilized for predicting future values or analyzing data. This course will give students an essential comprehension of the nature and basic processes used to analyze such data. The course will cover the theory and practice of time series analysis, with a focus on modeling and forecasting. Successfully completing assignments will require a combination of computing and statistics/mathematics. R will be utilized for computer-oriented analysis.

Schedule

	Week	Day	Topic(s)
1	M	Introduction to Time Series	Slides 1-16
	W	Introduction to R/RStudio/Quarto for Time Series
2	M	Practice Assignment 1 Group no meeting
	W	Introduction to R/RStudio/Quarto for Time Series
3	M	Labor Day 9/2 Holiday
	W	Zero-Mean Simple Models + Covariance	Slides 17-27
4	M	Stationary + ACF + MA(1)	Slides 28-38
	W	TS decomposition + Trend/Seasonality Estimation/Elimination	Slides 39-46
5	M	TS decomposition + Trend/Seasonality Estimation/Elimination
	W	Differencing Operator + Examples with R	Slides 47-52
6	M	TS Decomposition - Practice Assignment 2 Group - no meeting
	W	Stationary processes + ARMA(p,q)	Slides 75-91
7	M	Estimation Mean and ACF + ARMA models
	W	ARMA modeling
8	M	ARMA Practice Assignment 3 Group - no meeting
	W	Review -
9	M	Mid-term Exam Project no meeting
	W	ARIMA models	Slides 94-99
10	M	ARIMA models in R
	W	SARIMA models
11	M	SARIMA models - R Examples
	W	SARIMA Practice Assignment 4 Group - no meeting
12	M	Forecasting
	W	Forecasting Practice Assignment 5 Group - no meeting
13	M	Veteran Day 11/11 Holiday
	W	Regression with ARMA Errors
14	M	Regression TS Practice Assignment 6 Group - no meeting
	W	Additional topics
15	M	Review
	W	Thanksgiving
16	M	Final Exam on Monday - Final Week