Practical Time Series Analysis
- Offered byCoursera
Practical Time Series Analysis at Coursera Overview
Duration | 26 hours |
Start from | Start Now |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Practical Time Series Analysis at Coursera Highlights
- Earn a Certificate upon completion
- 100% online
- Start instantly and learn at your own schedule
- Flexible deadlines
- Reset deadlines in accordance to your schedule
Practical Time Series Analysis at Coursera Course details
- Time Series Forecasting
- Time Series
- Time Series Models
- 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.
Practical Time Series Analysis at Coursera Curriculum
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.
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.
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.
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.
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.
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.
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