Practical Time Series Analysis
The State University of New York
About this Course
Welcome to Practical Time Series Analysis!
Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics.
In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.
Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn.
You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself!
Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!
Skills you will gain
Time Series AnalysisTime Series ForecastingForecastingR Programming
Syllabus - What you will learn from this course
1
Section
WEEK 1: Basic Statistics
During this first week, we show how to download and install R on Windows and the Mac. We review those basics of inferential and descriptive statistics that you'll need during the course.
12 videos (Total 79 min), 4 readings, 2 quizzes
12 videos
Week 1 Welcome Video3m
Getting Started in R: Download and Install R on Windows5m
Getting Started in R: Download and Install R on Mac2m
Getting Started in R: Using Packages7m
Concatenation, Five-number summary, Standard Deviation5m
Histogram in R6m
Scatterplot in R3m
Review of Basic Statistics I - Simple Linear Regression6m
Reviewing Basic Statistics II More Linear Regression8m
Reviewing Basic Statistics III - Inference12m
Reviewing Basic Statistics IV9m
4 readings
Welcome to Week 11m
Getting Started with R10m
Basic Statistics Review (with linear regression and hypothesis testing)10m
Measuring Linear Association with the Correlation Function10m
2 practice exercises
Visualization4m
Basic Statistics Review18m
2
Section
Week 2: Visualizing Time Series, and Beginning to Model Time Series
In this week, we begin to explore and visualize time series available as acquired data sets. We also take our first steps on developing the mathematical models needed to analyze time series data.
10 videos (Total 54 min), 1 reading, 3 quizzes
10 videos
Introduction1m
Time plots8m
First Intuitions on (Weak) Stationarity2m
Autocovariance function9m
Autocovariance coefficients6m
Autocorrelation Function (ACF)5m
Random Walk9m
Introduction to Moving Average Processes3m
Simulating MA(2) process6m
1 readings
All slides together for the next two lessons10m
3 practice exercises
Noise Versus Signal4m
Random Walk vs Purely Random Process2m
Time plots, Stationarity, ACV, ACF, Random Walk and MA processes20m
3
Section
Week 3: Stationarity, MA(q) and AR(p) processes
In Week 3, we introduce few important notions in time series analysis: Stationarity, Backward shift operator, Invertibility, and Duality. We begin to explore Autoregressive processes and Yule-Walker equations.
13 videos (Total 112 min), 7 readings, 4 quizzes
13 videos
Stationarity - Intuition and Definition13m
Stationarity - First Examples...White Noise and Random Walks9m
Stationarity - First Examples...ACF of Moving Average10m
Series and Series Representation8m
Backward shift operator5m
Introduction to Invertibility12m
Duality9m
Mean Square Convergence (Optional)7m
Autoregressive Processes - Definition, Simulation, and First Examples9m
Autoregressive Processes - Backshift Operator and the ACF10m
Difference equations7m
Yule - Walker equations6m
7 readings
Stationarity - Examples -White Noise, Random Walks, and Moving Averages10m
Stationarity - Intuition and Definition10m
Stationarity - ACF of a Moving Average10m
All slides together for lesson 2 and 410m
Autoregressive Processes- Definition and First Examples10m
Autoregressive Processes - Backshift Operator and the ACF10m
Yule - Walker equations - Slides10m
4 practice exercises
Stationarity14m
Series, Backward Shift Operator, Invertibility and Duality30m
AR(p) and the ACF4m
Difference equations and Yule-Walker equations30m
4
Section
Week 4: AR(p) processes, Yule-Walker equations, PACF
In this week, partial autocorrelation is introduced. We work more on Yule-Walker equations, and apply what we have learned so far to few real-world datasets.
8 videos (Total 69 min), 3 readings, 3 quizzes
8 videos
Partial Autocorrelation and the PACF First Examples10m
Partial Autocorrelation and the PACF - Concept Development8m
Yule-Walker Equations in Matrix Form8m
Yule Walker Estimation - AR(2) Simulation17m
Yule Walker Estimation - AR(3) Simulation5m
Recruitment data - model fitting8m
Johnson & Johnson-model fitting8m
3 readings
Partial Autocorrelation and the PACF First Examples10m
Partial Autocorrelation and the PACF: Concept Development10m
All slides together for the next two lessons10m
3 practice exercises
Partial Autocorrelation4m
Yule-Walker in matrix form and Yule-Walker estimation20m
'LakeHuron' dataset40m
5
Section
Week 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models
In Week 5, we start working with Akaike Information criterion as a tool to judge our models, introduce mixed models such as ARMA, ARIMA and model few real-world datasets.
7 videos (Total 59 min), 6 readings, 4 quizzes
7 videos
Akaike Information Criterion and Model Quality11m
ARMA Models (And a Little Theory)9m
ARMA Properties and Examples9m
ARIMA Processes7m
Q-Statistic3m
Daily births in California in 195915m
6 readings
Akaike Information Criterion and Model Quality10m
ARMA Models and a Little Theory10m
ARMA Properties and Examples10m
All slides together for this lesson10m
Daily birth dataset10m
Daily female birth (R file)5m
4 practice exercises
AIC and model building4m
ARMA Processes6m
ARIMA and Q-statistic25m
'BJsales' dataset48m
6
Section
Week 6: Seasonality, SARIMA, Forecasting
In the last week of our course, another model is introduced: SARIMA. We fit SARIMA models to various datasets and start forecasting.
10 videos (Total 101 min), 6 readings, 3 quizzes
10 videos
SARIMA processes10m
ACF of SARIMA models10m
SARIMA fitting: Johnson & Johnson14m
SARIMA fitting: Milk production7m
SARIMA fitting: Sales at a souvenir shop12m
Forecasting Using Simple Exponential Smoothing12m
Double Exponential Smoothing11m
Triple Exponential Smoothing Concept Development10m
Triple Exponential Smoothing Implementation8m
6 readings
All slides together for the next two lessons10m
SARIMA simulation (code block)20m
SARIMA code for J&J (code block)10m
Forecasting using Simple Exponential Smoothing10m
Forecasting Using Holt Winters for Trend (Double Exponential)10m
Forecasting Using Holt Winters for Trend and Seasonality (Triple Exponential)10m
3 practice exercises
SARIMA processes25m
'USAccDeaths' dataset40m
Forecasting6m
4.6
83%
Top Reviews
By MS•Feb 28th 2018
I have not completed the course yet, working on week 5. If you have some Math background, this course gives a good practical introduction to Time Series Analysis. I recommend it.
By RM•Apr 23rd 2018
I would definitely recommend this course. It is a smooth introduction to time series analysis, very well explained and guided through multiple examples. I found it really useful!
Instructors
Tural Sadigov
Lecturer
William Thistleton
Associate ProfessorAbout The State University of New York
The State University of New York, with 64 unique institutions, is the largest comprehensive system of higher education in the United States. Educating nearly 468,000 students in more than 7,500 degree and certificate programs both on campus and online, SUNY has nearly 3 million alumni around the globe.
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