Practical Time Series Analysis is a time series analysis training course. Many of us are random data analysts. We are trained in science, business, or engineering, and now we find ourselves in a situation where we have data that we have not received any formal training to analyze. This course is suitable for people with little technical knowledge who want to become more familiar with this method of analysis.
In the practical analysis of time series, we will look at datasets that represent sequential information such as crop prices, annual rainfall, sunspot activity, and crop prices. We will also look at mathematical models that may be used to describe the process and generate this type of data. Then we take a look at graphical representations that show insights into our data. Eventually, we will learn how to make predictions that are relevant to smart cases and may occur in the future.
Skills you will learn in Practical Time Series Analysis:
- Predicting time series
- Time series
- Time series models
Instructor: Tural Sadigov and William Thistleton
Duration: About 26 hours to complete the course
Practical Time Series Analysis:
WEEK 1: Basic Statistics
Week 2: Visualizing Time Series, and Beginning to Model Time Series
Week 3: Stationarity, MA (q) and AR (p) processes
Week 4: AR (p) processes, Yule-Walker equations, PACF
Week 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models
Week 6: Seasonality, SARIMA, Forecasting
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